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WEBVTT
00:00:01.280 --> 00:00:06.759
so the class today is uh introduction to
00:00:04.680 --> 00:00:09.480
natural language processing and I'll be
00:00:06.759 --> 00:00:11.200
talking a little bit about you know what
00:00:09.480 --> 00:00:14.719
is natural language processing why we're
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motivated to do it and also some of the
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difficulties that we encounter and I'll
00:00:16.720 --> 00:00:19.880
at the end I'll also be talking about
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class Logistics so you can ask any
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Logistics questions at that
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time so if we talk about what is NLP
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anyway uh does anyone have any opinions
00:00:27.720 --> 00:00:31.439
about the definition of what natural
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language process would be oh one other
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thing I should mention is I am recording
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the class uh I put the class on YouTube
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uh afterwards I will not take pictures
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or video of any of you uh but if you
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talk your voice might come in the
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background so just uh be aware of that
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um usually not it's a directional mic so
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I try to repeat the questions after
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everybody um but uh for the people who
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are recordings uh listening to the
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recordings um so anyway what is NLP
00:00:57.680 --> 00:01:03.120
anyway does anybody have any ideas about
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the definition of what NLP might
00:01:06.119 --> 00:01:09.119
be
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yes okay um it so the answer was it
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helps machines understand language
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better uh so to facilitate human human
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and human machine interactions I think
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that's very good um it's
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uh similar to what I have written on my
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slide here uh but natur in addition to
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natural language understanding there's
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one major other segment of NLP uh does
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anyone uh have an idea what that might
00:01:48.719 --> 00:01:53.079
be we often have a dichotomy between two
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major segments natural language
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understanding and natural language
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generation yeah exactly so I I would say
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that's almost perfect if you had said
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understand and generate so very good um
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so I I say natural technology to handle
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human language usually text using
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computers uh to Aid human machine
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communication and this can include
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things like question answering dialogue
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or generation of code that can be
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executed with uh
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computers it can also Aid human human
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communication and this can include
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things like machine translation or spell
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checking or assisted writing
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and then a final uh segment that people
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might think about a little bit less is
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analyzing and understanding a language
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and this includes things like syntactic
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analysis text classification entity
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recognition and linking and these can be
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used for uh various reasons not
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necessarily for direct human machine
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communication but also for like
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aggregating information across large
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things for scientific studies and other
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things like that I'll give a few
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examples of
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this um we now use an many times a day
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sometimes without even knowing it so uh
00:03:04.040 --> 00:03:09.400
whenever you're typing a doc in Google
00:03:06.480 --> 00:03:11.599
Docs there's you know spell checking and
00:03:09.400 --> 00:03:13.959
grammar checking going on behind it's
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gotten frighten frighteningly good
00:03:13.959 --> 00:03:18.280
recently that where it checks like most
00:03:15.920 --> 00:03:20.720
of my mistakes and rarely Flags things
00:03:18.280 --> 00:03:22.799
that are not mistakes so obviously they
00:03:20.720 --> 00:03:25.080
have powerful models running behind that
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uh
00:03:25.640 --> 00:03:33.080
so and it can do things like answer
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questions uh so I asked chat GPT who is
00:03:33.080 --> 00:03:37.000
the current president of Carnegie melan
00:03:34.599 --> 00:03:38.920
University and chat GPT said I did a
00:03:37.000 --> 00:03:40.920
quick search for more information here
00:03:38.920 --> 00:03:43.439
is what I found uh the current president
00:03:40.920 --> 00:03:47.120
of car Mel University is faram Janan he
00:03:43.439 --> 00:03:50.040
has been serving since July 1 etc etc so
00:03:47.120 --> 00:03:50.040
as far as I can tell that's
00:03:50.400 --> 00:03:56.319
correct um at the same time I asked how
00:03:53.799 --> 00:04:00.280
many layers are included in the GP 3.5
00:03:56.319 --> 00:04:02.360
turbo architecture and it said to me
00:04:00.280 --> 00:04:05.400
GPT 3.5 turbo which is an optimized
00:04:02.360 --> 00:04:07.239
version of GPT 3.5 for faster responses
00:04:05.400 --> 00:04:08.959
doesn't have a specific layer art
00:04:07.239 --> 00:04:11.720
structure like the traditional gpt3
00:04:08.959 --> 00:04:13.560
models um and I don't know if this is
00:04:11.720 --> 00:04:16.600
true or not but I'm pretty sure it's not
00:04:13.560 --> 00:04:18.840
true I'm pretty sure that you know GPT
00:04:16.600 --> 00:04:20.560
is a model that's much like other models
00:04:18.840 --> 00:04:21.560
uh so it basically just made up the spec
00:04:20.560 --> 00:04:22.880
because it didn't have any information
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on the Internet or couldn't talk about
00:04:22.880 --> 00:04:26.000
it so
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um another thing is uh NLP can translate
00:04:29.639 --> 00:04:37.759
text pretty well so I ran um Google
00:04:33.479 --> 00:04:39.560
translate uh on Japanese uh this example
00:04:37.759 --> 00:04:41.639
is a little bit old it's from uh you
00:04:39.560 --> 00:04:44.639
know a few years ago about Co but I I
00:04:41.639 --> 00:04:46.240
retranslated it a few days ago and it
00:04:44.639 --> 00:04:47.680
comes up pretty good uh you can
00:04:46.240 --> 00:04:49.639
basically understand what's going on
00:04:47.680 --> 00:04:53.520
here it's not perfect but you can
00:04:49.639 --> 00:04:56.400
understand the uh the general uh
00:04:53.520 --> 00:04:58.560
gist at the same time uh if I put in a
00:04:56.400 --> 00:05:02.280
relatively low resource language this is
00:04:58.560 --> 00:05:05.759
Kurdish um it has a number of problems
00:05:02.280 --> 00:05:08.199
when you try to understand it and just
00:05:05.759 --> 00:05:12.400
to give an example this is talking about
00:05:08.199 --> 00:05:14.320
uh some uh paleontology Discovery it
00:05:12.400 --> 00:05:15.800
called this person a fossil scientist
00:05:14.320 --> 00:05:17.440
instead of the kind of obvious English
00:05:15.800 --> 00:05:20.120
term
00:05:17.440 --> 00:05:23.520
paleontologist um and it's talking about
00:05:20.120 --> 00:05:25.240
three different uh T-Rex species uh how
00:05:23.520 --> 00:05:27.039
T-Rex should actually be split into
00:05:25.240 --> 00:05:29.639
three species where T-Rex says king of
00:05:27.039 --> 00:05:31.560
ferocious lizards emperator says emperor
00:05:29.639 --> 00:05:33.720
of Savaged lizards and then T Regina
00:05:31.560 --> 00:05:35.120
means clean of ferocious snail I'm
00:05:33.720 --> 00:05:37.240
pretty sure that's not snail I'm pretty
00:05:35.120 --> 00:05:41.080
sure that's lizard so uh you can see
00:05:37.240 --> 00:05:41.080
that this is not uh this is not perfect
00:05:41.280 --> 00:05:46.680
either some people might be thinking why
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Google translate and why not GPD well it
00:05:46.680 --> 00:05:49.960
turns out um according to one of the
00:05:48.400 --> 00:05:51.759
recent studies we've done GPD is even
00:05:49.960 --> 00:05:55.479
worse at these slow resource languages
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so I use the best thing that's out
00:05:55.479 --> 00:06:00.440
there um another thing is language
00:05:58.120 --> 00:06:02.039
analysis can Aid scientific ific inquiry
00:06:00.440 --> 00:06:03.600
so this is an example that I've been
00:06:02.039 --> 00:06:06.120
using for a long time it's actually from
00:06:03.600 --> 00:06:09.160
Martin sap another faculty member here
00:06:06.120 --> 00:06:12.440
uh but I have been using it since uh
00:06:09.160 --> 00:06:14.160
like before he joined and it uh this is
00:06:12.440 --> 00:06:16.039
an example from computational social
00:06:14.160 --> 00:06:18.599
science uh answering questions about
00:06:16.039 --> 00:06:20.240
Society given observational data and
00:06:18.599 --> 00:06:22.280
their question was do movie scripts
00:06:20.240 --> 00:06:24.599
portray female or male characters with
00:06:22.280 --> 00:06:27.520
more power or agency in movie script
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films so it's asking kind of a so
00:06:27.520 --> 00:06:32.160
societal question by using NLP
00:06:30.120 --> 00:06:35.360
technology and the way they did it is
00:06:32.160 --> 00:06:36.880
they basically analyzed text trying to
00:06:35.360 --> 00:06:43.080
find
00:06:36.880 --> 00:06:45.280
uh the uh agents and patients in a a
00:06:43.080 --> 00:06:46.479
particular text which are the the things
00:06:45.280 --> 00:06:49.280
that are doing things and the things
00:06:46.479 --> 00:06:52.639
that things are being done to and you
00:06:49.280 --> 00:06:54.440
can see that essentially male characters
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in these movie scripts were given more
00:06:54.440 --> 00:06:58.080
power in agency and female characters
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were given less power in agency and they
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were able to do this because they had
00:06:59.960 --> 00:07:04.840
NLP technology that analyzed and
00:07:02.680 --> 00:07:08.960
extracted useful data and made turned it
00:07:04.840 --> 00:07:11.520
into a very easy form to do kind of
00:07:08.960 --> 00:07:15.840
analysis of the variety that they want
00:07:11.520 --> 00:07:17.400
so um I think that's a major use case of
00:07:15.840 --> 00:07:19.400
NLP technology that does language
00:07:17.400 --> 00:07:20.919
analysis nowadays turn it into a form
00:07:19.400 --> 00:07:23.960
that allows you to very quickly do
00:07:20.919 --> 00:07:27.440
aggregate queries and other things like
00:07:23.960 --> 00:07:30.479
this um but at the same time uh language
00:07:27.440 --> 00:07:33.520
analysis tools fail at very basic tasks
00:07:30.479 --> 00:07:36.000
so these are
00:07:33.520 --> 00:07:38.199
some things that I ran through a named
00:07:36.000 --> 00:07:41.080
entity recognizer and these were kind of
00:07:38.199 --> 00:07:43.160
very nice named entity recognizers uh
00:07:41.080 --> 00:07:46.240
that a lot of people were using for
00:07:43.160 --> 00:07:48.039
example Stanford core NLP and Spacey and
00:07:46.240 --> 00:07:50.319
both of them I just threw in the first
00:07:48.039 --> 00:07:53.120
thing that I found on the New York Times
00:07:50.319 --> 00:07:55.199
at the time and it basically made at
00:07:53.120 --> 00:07:58.319
least one mistake in the first sentence
00:07:55.199 --> 00:08:00.840
and here it recognizes Baton Rouge as an
00:07:58.319 --> 00:08:04.720
organization and here it recognized
00:08:00.840 --> 00:08:07.000
hurricane EA as an organization so um
00:08:04.720 --> 00:08:08.879
like even uh these things that we expect
00:08:07.000 --> 00:08:10.360
should work pretty well make pretty
00:08:08.879 --> 00:08:13.360
Solly
00:08:10.360 --> 00:08:16.199
mistakes so in the class uh basically
00:08:13.360 --> 00:08:18.479
what I want to cover is uh what goes
00:08:16.199 --> 00:08:20.360
into building uh state-of-the-art NLP
00:08:18.479 --> 00:08:24.000
systems that work really well on a wide
00:08:20.360 --> 00:08:26.240
variety of tasks um where do current
00:08:24.000 --> 00:08:28.840
systems
00:08:26.240 --> 00:08:30.479
fail and how can we make appropriate
00:08:28.840 --> 00:08:35.000
improvements and Achieve whatever we
00:08:30.479 --> 00:08:37.719
want to do with nalp and this set of
00:08:35.000 --> 00:08:39.360
questions that I'm asking here is
00:08:37.719 --> 00:08:40.919
exactly the same as the set of questions
00:08:39.360 --> 00:08:43.519
that I was asking two years ago before
00:08:40.919 --> 00:08:45.480
chat GPT uh I still think they're
00:08:43.519 --> 00:08:46.920
important questions but I think the
00:08:45.480 --> 00:08:48.399
answers to these questions is very
00:08:46.920 --> 00:08:50.040
different and because of that we're
00:08:48.399 --> 00:08:52.120
updating the class materials to try to
00:08:50.040 --> 00:08:54.399
cover you know the answers to these
00:08:52.120 --> 00:08:56.000
questions and uh in kind of the era of
00:08:54.399 --> 00:08:58.200
large language models and other things
00:08:56.000 --> 00:08:59.720
like
00:08:58.200 --> 00:09:02.079
that
00:08:59.720 --> 00:09:03.360
so that's all I have for the intro maybe
00:09:02.079 --> 00:09:06.640
maybe pretty straightforward are there
00:09:03.360 --> 00:09:08.480
any questions or comments so far if not
00:09:06.640 --> 00:09:14.399
I'll I'll just go
00:09:08.480 --> 00:09:17.160
on okay great so I want to uh first go
00:09:14.399 --> 00:09:19.480
into a very high Lev overview of NLP
00:09:17.160 --> 00:09:20.839
system building and most of the stuff
00:09:19.480 --> 00:09:22.399
that I want to do today is to set the
00:09:20.839 --> 00:09:24.320
stage for what I'm going to be talking
00:09:22.399 --> 00:09:25.040
about in more detail uh over the rest of
00:09:24.320 --> 00:09:29.200
the
00:09:25.040 --> 00:09:31.720
class and we could think of NLP syst
00:09:29.200 --> 00:09:34.040
systems through this kind of General
00:09:31.720 --> 00:09:36.560
framework where we want to create a
00:09:34.040 --> 00:09:40.600
function to map an input X into an
00:09:36.560 --> 00:09:44.440
output y uh where X and or Y involve
00:09:40.600 --> 00:09:47.000
language and uh do some people have
00:09:44.440 --> 00:09:50.120
favorite NLP tasks or NLP tasks that you
00:09:47.000 --> 00:09:52.399
want to uh want to be handling in some
00:09:50.120 --> 00:09:57.000
way or maybe what what do you think are
00:09:52.399 --> 00:09:57.000
the most popular and important NLP tasks
00:09:58.120 --> 00:10:03.200
nowadays
00:10:00.800 --> 00:10:06.120
okay so translation is maybe easy what's
00:10:03.200 --> 00:10:06.120
the input and output of
00:10:11.440 --> 00:10:15.720
translation okay yeah so uh in
00:10:13.800 --> 00:10:17.959
Translation inputs text in one language
00:10:15.720 --> 00:10:21.760
output is text in another language and
00:10:17.959 --> 00:10:21.760
then what what is a good
00:10:27.680 --> 00:10:32.160
translation yeah corre or or the same is
00:10:30.320 --> 00:10:35.839
the input basically yes um it also
00:10:32.160 --> 00:10:37.760
should be fluent but I agree any other
00:10:35.839 --> 00:10:39.839
things generation the reason why I said
00:10:37.760 --> 00:10:41.519
it's tough is it's pretty broad um and
00:10:39.839 --> 00:10:43.360
it's not like we could be doing
00:10:41.519 --> 00:10:46.360
generation with lots of different inputs
00:10:43.360 --> 00:10:51.440
but um yeah any any other things maybe a
00:10:46.360 --> 00:10:51.440
little bit different yeah like
00:10:51.480 --> 00:10:55.959
scenario a scenario and a multiple
00:10:54.000 --> 00:10:58.200
choice question about the scenario and
00:10:55.959 --> 00:10:59.680
so what would the scenario in the
00:10:58.200 --> 00:11:01.760
multiple choice question are probably
00:10:59.680 --> 00:11:04.040
the input and then the output
00:11:01.760 --> 00:11:06.480
is an answer to the multiple choice
00:11:04.040 --> 00:11:07.920
question um and then there it's kind of
00:11:06.480 --> 00:11:12.279
obvious like what is good it's the
00:11:07.920 --> 00:11:14.880
correct answer sure um interestingly I
00:11:12.279 --> 00:11:17.440
think a lot of llm evaluation is done on
00:11:14.880 --> 00:11:21.160
these multiple choice questions but I'm
00:11:17.440 --> 00:11:22.320
yet to encounter an actual application
00:11:21.160 --> 00:11:24.880
that cares about multiple choice
00:11:22.320 --> 00:11:26.880
question answering so uh there's kind of
00:11:24.880 --> 00:11:30.959
a funny disconnect there but uh yeah I
00:11:26.880 --> 00:11:33.519
saw hand that think about V search comp
00:11:30.959 --> 00:11:36.360
yeah Vector search uh that's very good
00:11:33.519 --> 00:11:36.360
so the input
00:11:37.120 --> 00:11:45.000
is can con it into or understanding and
00:11:42.560 --> 00:11:45.000
it to
00:11:47.360 --> 00:11:53.760
another okay yeah so I'd say the input
00:11:49.880 --> 00:11:56.160
there is a query and a document base um
00:11:53.760 --> 00:11:57.959
and then the output is maybe an index
00:11:56.160 --> 00:11:59.800
into the document or or something else
00:11:57.959 --> 00:12:01.279
like that sure um and then something
00:11:59.800 --> 00:12:05.040
that's good here here's a good question
00:12:01.279 --> 00:12:05.040
what what's a good result from
00:12:06.560 --> 00:12:10.200
that what's a good
00:12:10.839 --> 00:12:19.279
output be sort of simar the major
00:12:15.560 --> 00:12:21.680
problem there I see is how you def SAR
00:12:19.279 --> 00:12:26.199
and how you
00:12:21.680 --> 00:12:29.760
a always like you understand
00:12:26.199 --> 00:12:33.000
whether is actually
00:12:29.760 --> 00:12:35.079
yeah exactly so that um just to repeat
00:12:33.000 --> 00:12:36.880
it's like uh we need to have a
00:12:35.079 --> 00:12:38.399
similarity a good similarity metric we
00:12:36.880 --> 00:12:40.120
need to have a good threshold where we
00:12:38.399 --> 00:12:41.760
get like the ones we want and we don't
00:12:40.120 --> 00:12:43.240
get the ones we don't want we're going
00:12:41.760 --> 00:12:44.959
to talk more about that in the retrieval
00:12:43.240 --> 00:12:48.440
lecture exactly how we evaluate and
00:12:44.959 --> 00:12:49.920
stuff but um yeah good so this is a good
00:12:48.440 --> 00:12:53.279
uh here are some good examples I have
00:12:49.920 --> 00:12:55.519
some examples of my own um the first one
00:12:53.279 --> 00:12:58.360
is uh kind of the very generic one maybe
00:12:55.519 --> 00:13:00.800
kind of like generation here but text in
00:12:58.360 --> 00:13:02.959
continuing text uh so this is language
00:13:00.800 --> 00:13:04.160
modeling so you have a text and then you
00:13:02.959 --> 00:13:05.440
have the continuation you want to
00:13:04.160 --> 00:13:07.680
predict the
00:13:05.440 --> 00:13:10.480
continuation um text and text in another
00:13:07.680 --> 00:13:13.040
language is translation uh text in a
00:13:10.480 --> 00:13:15.800
label could be text classification uh
00:13:13.040 --> 00:13:17.760
text in linguistic structure or uh some
00:13:15.800 --> 00:13:21.360
s kind of entities or something like
00:13:17.760 --> 00:13:22.680
that could be uh language analysis or um
00:13:21.360 --> 00:13:24.839
information
00:13:22.680 --> 00:13:29.440
extraction uh we could also have image
00:13:24.839 --> 00:13:31.320
and text uh which is image captioning um
00:13:29.440 --> 00:13:33.560
or speech and text which is speech
00:13:31.320 --> 00:13:35.240
recognition and I take the very broad
00:13:33.560 --> 00:13:38.000
view of natural language processing
00:13:35.240 --> 00:13:39.519
which is if it's any variety of language
00:13:38.000 --> 00:13:41.519
uh if you're handling language in some
00:13:39.519 --> 00:13:42.800
way it's natural language processing it
00:13:41.519 --> 00:13:45.880
doesn't necessarily have to be text
00:13:42.800 --> 00:13:47.480
input text output um so that's relevant
00:13:45.880 --> 00:13:50.199
for the projects that you're thinking
00:13:47.480 --> 00:13:52.160
about too at the end of this course so
00:13:50.199 --> 00:13:55.519
the the most common FAQ for this course
00:13:52.160 --> 00:13:57.839
is does my project count and if you're
00:13:55.519 --> 00:13:59.360
uncertain you should ask but usually
00:13:57.839 --> 00:14:01.040
like if it has some sort of language
00:13:59.360 --> 00:14:05.079
involved then I'll usually say yes it
00:14:01.040 --> 00:14:07.920
does kind so um if it's like uh code to
00:14:05.079 --> 00:14:09.680
code there that's not code is not
00:14:07.920 --> 00:14:11.480
natural language it is language but it's
00:14:09.680 --> 00:14:13.000
not natural language so that might be
00:14:11.480 --> 00:14:15.320
borderline we might have to discuss
00:14:13.000 --> 00:14:15.320
about
00:14:15.759 --> 00:14:21.800
that cool um so next I'd like to talk
00:14:18.880 --> 00:14:25.240
about methods for creating NLP systems
00:14:21.800 --> 00:14:27.839
um and there's a lot of different ways
00:14:25.240 --> 00:14:29.720
to create MLP systems all of these are
00:14:27.839 --> 00:14:32.880
alive and well in
00:14:29.720 --> 00:14:35.759
2024 uh the first one is Rule uh
00:14:32.880 --> 00:14:37.959
rule-based system creation and so the
00:14:35.759 --> 00:14:40.399
way this works is like let's say you
00:14:37.959 --> 00:14:42.480
want to build a text classifier you just
00:14:40.399 --> 00:14:46.560
write the simple python function that
00:14:42.480 --> 00:14:48.639
classifies things into uh sports or
00:14:46.560 --> 00:14:50.240
other and the way it classifies it into
00:14:48.639 --> 00:14:52.959
sports or other is it checks whether
00:14:50.240 --> 00:14:55.160
baseball soccer football and Tennis are
00:14:52.959 --> 00:14:59.399
included in the document and classifies
00:14:55.160 --> 00:15:01.959
it into uh Sports if so uh other if not
00:14:59.399 --> 00:15:05.279
so has anyone written something like
00:15:01.959 --> 00:15:09.720
this maybe not a text classifier but um
00:15:05.279 --> 00:15:11.880
you know to identify entities or uh
00:15:09.720 --> 00:15:14.279
split words
00:15:11.880 --> 00:15:16.680
or something like
00:15:14.279 --> 00:15:18.399
that has anybody not ever written
00:15:16.680 --> 00:15:22.800
anything like
00:15:18.399 --> 00:15:24.639
this yeah that's what I thought so um
00:15:22.800 --> 00:15:26.079
rule-based systems are very convenient
00:15:24.639 --> 00:15:28.920
when you don't really care about how
00:15:26.079 --> 00:15:30.759
good your system is um or you're doing
00:15:28.920 --> 00:15:32.360
that's really really simple and like
00:15:30.759 --> 00:15:35.600
it'll be perfect even if you do the very
00:15:32.360 --> 00:15:37.079
simple thing and so I I think it's worth
00:15:35.600 --> 00:15:39.959
talking a little bit about them and I'll
00:15:37.079 --> 00:15:43.319
talk a little bit about that uh this
00:15:39.959 --> 00:15:45.680
time the second thing which like very
00:15:43.319 --> 00:15:47.680
rapidly over the course of maybe three
00:15:45.680 --> 00:15:50.279
years or so has become actually maybe
00:15:47.680 --> 00:15:52.720
the dominant Paradigm in NLP is
00:15:50.279 --> 00:15:56.360
prompting uh in prompting a language
00:15:52.720 --> 00:15:58.560
model and the way this works is uh you
00:15:56.360 --> 00:16:00.720
ask a language model if the following
00:15:58.560 --> 00:16:03.079
sent is about sports reply Sports
00:16:00.720 --> 00:16:06.120
otherwise reply other and you feed it to
00:16:03.079 --> 00:16:08.480
your favorite LM uh usually that's GPT
00:16:06.120 --> 00:16:11.399
something or other uh sometimes it's an
00:16:08.480 --> 00:16:14.440
open source model of some variety and
00:16:11.399 --> 00:16:17.759
then uh it will give you the
00:16:14.440 --> 00:16:20.639
answer and then finally uh fine-tuning
00:16:17.759 --> 00:16:22.240
uh so you take some paired data and you
00:16:20.639 --> 00:16:23.600
do machine learning from paired data
00:16:22.240 --> 00:16:25.680
where you have something like I love to
00:16:23.600 --> 00:16:27.440
play baseball uh the stock price is
00:16:25.680 --> 00:16:29.519
going up he got a hatrick yesterday he
00:16:27.440 --> 00:16:32.759
is wearing tennis shoes and you assign
00:16:29.519 --> 00:16:35.319
all these uh labels to them training a
00:16:32.759 --> 00:16:38.160
model and you can even start out with a
00:16:35.319 --> 00:16:41.480
prompting based model and fine-tune a a
00:16:38.160 --> 00:16:41.480
language model
00:16:42.920 --> 00:16:49.399
also so one major consideration when
00:16:47.519 --> 00:16:52.000
you're Building Systems like this is the
00:16:49.399 --> 00:16:56.440
data requirements for building such a
00:16:52.000 --> 00:16:59.319
system and for rules or prompting where
00:16:56.440 --> 00:17:02.240
it's just based on intuition really no
00:16:59.319 --> 00:17:04.640
data is needed whatsoever it you don't
00:17:02.240 --> 00:17:08.240
need a single example and you can start
00:17:04.640 --> 00:17:11.000
writing rules or like just just to give
00:17:08.240 --> 00:17:12.640
an example the rules and prompts I wrote
00:17:11.000 --> 00:17:14.679
here I didn't look at any examples and I
00:17:12.640 --> 00:17:17.240
just wrote them uh so this is something
00:17:14.679 --> 00:17:20.000
that you could start out
00:17:17.240 --> 00:17:21.559
with uh the problem is you also have no
00:17:20.000 --> 00:17:24.720
idea how well it works if you don't have
00:17:21.559 --> 00:17:26.760
any data whatsoever right so um you'll
00:17:24.720 --> 00:17:30.400
you might be in trouble if you think
00:17:26.760 --> 00:17:30.400
something should be working
00:17:30.919 --> 00:17:34.440
so normally the next thing that people
00:17:32.919 --> 00:17:36.880
move to nowadays when they're building
00:17:34.440 --> 00:17:39.559
practical systems is rules are prompting
00:17:36.880 --> 00:17:41.240
based on spot checks so that basically
00:17:39.559 --> 00:17:42.919
means that you start out with a
00:17:41.240 --> 00:17:45.840
rule-based system or a prompting based
00:17:42.919 --> 00:17:47.240
system and then you go in and you run it
00:17:45.840 --> 00:17:48.720
on some data that you're interested in
00:17:47.240 --> 00:17:50.799
you just kind of qualitatively look at
00:17:48.720 --> 00:17:52.160
the data and say oh it's messing up here
00:17:50.799 --> 00:17:53.440
then you go in and fix your prompt a
00:17:52.160 --> 00:17:54.919
little bit or you go in and fix your
00:17:53.440 --> 00:17:57.320
rules a little bit or something like
00:17:54.919 --> 00:18:00.400
that so uh this is kind of the second
00:17:57.320 --> 00:18:00.400
level of difficulty
00:18:01.400 --> 00:18:04.640
so the third level of difficulty would
00:18:03.159 --> 00:18:07.400
be something like rules are prompting
00:18:04.640 --> 00:18:09.039
with rigorous evaluation and so here you
00:18:07.400 --> 00:18:12.840
would create a development set with
00:18:09.039 --> 00:18:14.840
inputs and outputs uh so you uh create
00:18:12.840 --> 00:18:17.039
maybe 200 to 2,000
00:18:14.840 --> 00:18:20.080
examples um
00:18:17.039 --> 00:18:21.720
and then evaluate your actual accuracy
00:18:20.080 --> 00:18:23.880
so you need an evaluation metric you
00:18:21.720 --> 00:18:26.120
need other things like this this is the
00:18:23.880 --> 00:18:28.400
next level of difficulty but if you're
00:18:26.120 --> 00:18:30.240
going to be a serious you know NLP
00:18:28.400 --> 00:18:33.000
engineer or something like this you
00:18:30.240 --> 00:18:34.720
definitely will be doing this a lot I
00:18:33.000 --> 00:18:37.760
feel and
00:18:34.720 --> 00:18:40.360
then so that here now you start needing
00:18:37.760 --> 00:18:41.960
a depth set and a test set and then
00:18:40.360 --> 00:18:46.280
finally fine-tuning you need an
00:18:41.960 --> 00:18:48.480
additional training set um and uh this
00:18:46.280 --> 00:18:52.240
will generally be a lot bigger than 200
00:18:48.480 --> 00:18:56.080
to 2,000 examples and generally the rule
00:18:52.240 --> 00:18:56.080
is that every time you
00:18:57.320 --> 00:19:01.080
double
00:18:59.520 --> 00:19:02.400
every time you double your training set
00:19:01.080 --> 00:19:07.480
size you get about a constant
00:19:02.400 --> 00:19:07.480
Improvement so if you start
00:19:07.799 --> 00:19:15.080
out if you start out down here with
00:19:12.240 --> 00:19:17.039
um zero shot accuracy with a language
00:19:15.080 --> 00:19:21.559
model you you create a small printing
00:19:17.039 --> 00:19:21.559
set and you get you know a pretty big
00:19:22.000 --> 00:19:29.120
increase and then every time you double
00:19:26.320 --> 00:19:30.799
it it increases by constant fact it's
00:19:29.120 --> 00:19:32.480
kind of like just in general in machine
00:19:30.799 --> 00:19:37.360
learning this is a trend that we tend to
00:19:32.480 --> 00:19:40.679
see so um So based on this
00:19:37.360 --> 00:19:41.880
uh there's kind of like you get a big
00:19:40.679 --> 00:19:44.200
gain from having a little bit of
00:19:41.880 --> 00:19:45.760
training data but the gains very quickly
00:19:44.200 --> 00:19:48.919
drop off and you start spending a lot of
00:19:45.760 --> 00:19:48.919
time annotating
00:19:51.000 --> 00:19:55.880
an so um yeah this is the the general
00:19:54.760 --> 00:19:58.280
overview of the different types of
00:19:55.880 --> 00:20:00.000
system building uh any any question
00:19:58.280 --> 00:20:01.559
questions about this or comments or
00:20:00.000 --> 00:20:04.000
things like
00:20:01.559 --> 00:20:05.840
this I think one thing that's changed
00:20:04.000 --> 00:20:08.159
really drastically from the last time I
00:20:05.840 --> 00:20:09.600
taught this class is the fact that
00:20:08.159 --> 00:20:11.000
number one and number two are the things
00:20:09.600 --> 00:20:13.799
that people are actually doing in
00:20:11.000 --> 00:20:15.360
practice uh which was you know people
00:20:13.799 --> 00:20:16.679
who actually care about systems are
00:20:15.360 --> 00:20:18.880
doing number one and number two is the
00:20:16.679 --> 00:20:20.440
main thing it used to be that if you
00:20:18.880 --> 00:20:22.679
were actually serious about building a
00:20:20.440 --> 00:20:24.320
system uh you really needed to do the
00:20:22.679 --> 00:20:27.080
funing and now it's kind of like more
00:20:24.320 --> 00:20:27.080
optional
00:20:27.159 --> 00:20:30.159
so
00:20:44.039 --> 00:20:50.960
yeah
00:20:46.320 --> 00:20:53.960
so it's it's definitely an empirical
00:20:50.960 --> 00:20:53.960
observation
00:20:54.720 --> 00:21:01.080
um in terms of the theoretical
00:20:57.640 --> 00:21:03.120
background I am not I can't immediately
00:21:01.080 --> 00:21:05.840
point to a
00:21:03.120 --> 00:21:10.039
particular paper that does that but I
00:21:05.840 --> 00:21:12.720
think if you think about
00:21:10.039 --> 00:21:14.720
the I I think I have seen that they do
00:21:12.720 --> 00:21:17.039
exist in the past but I I can't think of
00:21:14.720 --> 00:21:19.000
it right now I can try to uh try to come
00:21:17.039 --> 00:21:23.720
up with an example of
00:21:19.000 --> 00:21:23.720
that so yeah I I should take
00:21:26.799 --> 00:21:31.960
notes or someone wants to share one on
00:21:29.360 --> 00:21:33.360
Piaza uh if you have any ideas and want
00:21:31.960 --> 00:21:34.520
to share on Patza I'm sure that would be
00:21:33.360 --> 00:21:35.640
great it'd be great to have a discussion
00:21:34.520 --> 00:21:39.320
on
00:21:35.640 --> 00:21:44.960
Patza um Pi
00:21:39.320 --> 00:21:46.880
one cool okay so next I want to try to
00:21:44.960 --> 00:21:48.200
make a rule-based system and I'm going
00:21:46.880 --> 00:21:49.360
to make a rule-based system for
00:21:48.200 --> 00:21:51.799
sentiment
00:21:49.360 --> 00:21:53.480
analysis uh and this is a bad idea I
00:21:51.799 --> 00:21:55.400
would not encourage you to ever do this
00:21:53.480 --> 00:21:57.440
in real life but I want to do it here to
00:21:55.400 --> 00:21:59.640
show you why it's a bad idea and like
00:21:57.440 --> 00:22:01.200
what are some of the hard problems that
00:21:59.640 --> 00:22:03.960
you encounter when trying to create a
00:22:01.200 --> 00:22:06.600
system based on rules
00:22:03.960 --> 00:22:08.080
and then we'll move into building a
00:22:06.600 --> 00:22:12.360
machine learning base system after we
00:22:08.080 --> 00:22:15.400
finish this so if we look at the example
00:22:12.360 --> 00:22:18.559
test this is review sentiment analysis
00:22:15.400 --> 00:22:21.799
it's one of the most valuable uh tasks
00:22:18.559 --> 00:22:24.039
uh that people do in NLP nowadays
00:22:21.799 --> 00:22:26.400
because it allows people to know how
00:22:24.039 --> 00:22:29.200
customers are thinking about products uh
00:22:26.400 --> 00:22:30.799
improve their you know their product
00:22:29.200 --> 00:22:32.919
development and other things like that
00:22:30.799 --> 00:22:34.799
may monitor people's you know
00:22:32.919 --> 00:22:36.760
satisfaction with their social media
00:22:34.799 --> 00:22:39.200
service other things like this so
00:22:36.760 --> 00:22:42.720
basically the way it works is um you
00:22:39.200 --> 00:22:44.400
have uh outputs or you have sentences
00:22:42.720 --> 00:22:46.720
inputs like I hate this movie I love
00:22:44.400 --> 00:22:48.520
this movie I saw this movie and this
00:22:46.720 --> 00:22:50.600
gets mapped into positive neutral or
00:22:48.520 --> 00:22:53.120
negative so I hate this movie would be
00:22:50.600 --> 00:22:55.480
negative I love this movie positive and
00:22:53.120 --> 00:22:59.039
I saw this movie is
00:22:55.480 --> 00:23:01.200
neutral so um
00:22:59.039 --> 00:23:05.200
that that's the task input tax output
00:23:01.200 --> 00:23:08.880
labels uh Kary uh sentence
00:23:05.200 --> 00:23:11.679
label and in order to do this uh we
00:23:08.880 --> 00:23:13.120
would like to build a model um and we're
00:23:11.679 --> 00:23:16.159
going to build the model in a rule based
00:23:13.120 --> 00:23:19.000
way but it we'll still call it a model
00:23:16.159 --> 00:23:21.600
and the way it works is we do feature
00:23:19.000 --> 00:23:23.159
extraction um so we extract the Salient
00:23:21.600 --> 00:23:25.279
features for making the decision about
00:23:23.159 --> 00:23:27.320
what to Output next we do score
00:23:25.279 --> 00:23:29.880
calculation calculate a score for one or
00:23:27.320 --> 00:23:32.320
more possib ities and we have a decision
00:23:29.880 --> 00:23:33.520
function so we choose one of those
00:23:32.320 --> 00:23:37.679
several
00:23:33.520 --> 00:23:40.120
possibilities and so for feature
00:23:37.679 --> 00:23:42.200
extraction uh formally what this looks
00:23:40.120 --> 00:23:44.240
like is we have some function and it
00:23:42.200 --> 00:23:48.039
extracts a feature
00:23:44.240 --> 00:23:51.159
Vector for score calculation um we
00:23:48.039 --> 00:23:54.240
calculate the scores based on either a
00:23:51.159 --> 00:23:56.279
binary classification uh where we have a
00:23:54.240 --> 00:23:58.279
a weight vector and we take the dot
00:23:56.279 --> 00:24:00.120
product with our feature vector or we
00:23:58.279 --> 00:24:02.480
have multi class classification where we
00:24:00.120 --> 00:24:04.520
have a weight Matrix and we take the
00:24:02.480 --> 00:24:08.640
product with uh the vector and that
00:24:04.520 --> 00:24:08.640
gives us you know squares over multiple
00:24:08.919 --> 00:24:14.840
classes and then we have a decision uh
00:24:11.600 --> 00:24:17.520
rule so this decision rule tells us what
00:24:14.840 --> 00:24:20.080
the output is going to be um does anyone
00:24:17.520 --> 00:24:22.200
know what a typical decision rule is
00:24:20.080 --> 00:24:24.520
maybe maybe so obvious that you don't
00:24:22.200 --> 00:24:28.760
think about it often
00:24:24.520 --> 00:24:31.000
but uh a threshold um so like for would
00:24:28.760 --> 00:24:34.440
that be for binary a single binary
00:24:31.000 --> 00:24:37.000
scaler score or a multiple
00:24:34.440 --> 00:24:38.520
class binary yeah so and then you would
00:24:37.000 --> 00:24:39.960
pick a threshold and if it's over the
00:24:38.520 --> 00:24:42.919
threshold
00:24:39.960 --> 00:24:45.760
you say yes and if it's under the
00:24:42.919 --> 00:24:50.279
threshold you say no um another option
00:24:45.760 --> 00:24:51.679
would be um you have a threshold and you
00:24:50.279 --> 00:24:56.080
say
00:24:51.679 --> 00:24:56.080
yes no
00:24:56.200 --> 00:25:00.559
obain so you know you don't give an
00:24:58.360 --> 00:25:02.520
answer and depending on how you're
00:25:00.559 --> 00:25:03.720
evaluated what what is a good classifier
00:25:02.520 --> 00:25:07.799
you might want to abstain some of the
00:25:03.720 --> 00:25:10.960
time also um for multiclass what what's
00:25:07.799 --> 00:25:10.960
a standard decision role for
00:25:11.120 --> 00:25:16.720
multiclass argmax yeah exactly so um
00:25:14.279 --> 00:25:19.520
basically you you find the index that
00:25:16.720 --> 00:25:22.000
has the highest score in you output
00:25:19.520 --> 00:25:24.480
it we're going to be talking about other
00:25:22.000 --> 00:25:26.559
decision rules also um like
00:25:24.480 --> 00:25:29.480
self-consistency and minimum based risk
00:25:26.559 --> 00:25:30.760
later uh for text generation so you can
00:25:29.480 --> 00:25:33.000
just keep that in mind and then we'll
00:25:30.760 --> 00:25:36.279
forget about it for like several
00:25:33.000 --> 00:25:39.559
classes um so for sentiment
00:25:36.279 --> 00:25:42.159
class um I have a Cod
00:25:39.559 --> 00:25:45.159
walk
00:25:42.159 --> 00:25:45.159
here
00:25:46.240 --> 00:25:54.320
and this is pretty simple um but if
00:25:50.320 --> 00:25:58.559
you're bored uh of the class and would
00:25:54.320 --> 00:26:01.000
like to um try out yourself you can
00:25:58.559 --> 00:26:04.480
Challenge and try to get a better score
00:26:01.000 --> 00:26:06.120
than I do um over the next few minutes
00:26:04.480 --> 00:26:06.880
but we have this rule based classifier
00:26:06.120 --> 00:26:10.240
in
00:26:06.880 --> 00:26:12.640
here and I will open it up in my vs
00:26:10.240 --> 00:26:15.360
code
00:26:12.640 --> 00:26:18.360
to try to create a rule-based classifier
00:26:15.360 --> 00:26:18.360
and basically the way this
00:26:22.799 --> 00:26:29.960
works is
00:26:25.159 --> 00:26:29.960
that we have a feature
00:26:31.720 --> 00:26:37.720
extraction we have feature extraction we
00:26:34.120 --> 00:26:40.679
have scoring and we have um a decision
00:26:37.720 --> 00:26:43.480
rle so here for our feature extraction I
00:26:40.679 --> 00:26:44.720
have created a list of good words and a
00:26:43.480 --> 00:26:46.720
list of bad
00:26:44.720 --> 00:26:48.960
words
00:26:46.720 --> 00:26:51.320
and what we do is we just count the
00:26:48.960 --> 00:26:53.000
number of good words that appeared and
00:26:51.320 --> 00:26:55.320
count the number of bad words that
00:26:53.000 --> 00:26:57.880
appeared then we also have a bias
00:26:55.320 --> 00:27:01.159
feature so the bias feature is a feature
00:26:57.880 --> 00:27:03.679
that's always one and so what that
00:27:01.159 --> 00:27:06.799
results in is we have a dimension three
00:27:03.679 --> 00:27:08.880
feature Vector um where this is like the
00:27:06.799 --> 00:27:11.320
number of good words this is the number
00:27:08.880 --> 00:27:15.320
of bad words and then you have the
00:27:11.320 --> 00:27:17.760
bias and then I also Define the feature
00:27:15.320 --> 00:27:20.039
weights that so for every good word we
00:27:17.760 --> 00:27:22.200
add one to our score for every bad word
00:27:20.039 --> 00:27:25.559
we add uh we subtract one from our score
00:27:22.200 --> 00:27:29.399
and for the BIOS we absor and so we then
00:27:25.559 --> 00:27:30.480
take the dot product between
00:27:29.399 --> 00:27:34.360
these
00:27:30.480 --> 00:27:36.919
two and we get minus
00:27:34.360 --> 00:27:37.640
0.5 and that gives us uh that gives us
00:27:36.919 --> 00:27:41.000
the
00:27:37.640 --> 00:27:46.000
squore so let's run
00:27:41.000 --> 00:27:50.320
that um and I read in some
00:27:46.000 --> 00:27:52.600
data and what this data looks like is
00:27:50.320 --> 00:27:55.000
basically we have a
00:27:52.600 --> 00:27:57.559
review um which says the rock is
00:27:55.000 --> 00:27:59.480
destined to be the 21st Century's new
00:27:57.559 --> 00:28:01.240
Conan and that he's going to make a
00:27:59.480 --> 00:28:03.600
splash even greater than Arnold
00:28:01.240 --> 00:28:07.000
Schwarzenegger jeanclaude vanam or
00:28:03.600 --> 00:28:09.519
Steven Seagal um so this seems pretty
00:28:07.000 --> 00:28:10.840
positive right I like that's a pretty
00:28:09.519 --> 00:28:13.200
high order to be better than Arnold
00:28:10.840 --> 00:28:16.080
Schwarzenegger or John Claude vanam uh
00:28:13.200 --> 00:28:19.519
if you're familiar with action movies um
00:28:16.080 --> 00:28:22.840
and so of course this gets a positive
00:28:19.519 --> 00:28:24.120
label and so uh we have run classifier
00:28:22.840 --> 00:28:25.240
actually maybe I should call this
00:28:24.120 --> 00:28:27.600
decision rule because this is
00:28:25.240 --> 00:28:29.120
essentially our decision Rule and here
00:28:27.600 --> 00:28:32.600
basically do the thing that I mentioned
00:28:29.120 --> 00:28:35.440
here the yes no obstain or in this case
00:28:32.600 --> 00:28:38.360
positive negative neutral so if the
00:28:35.440 --> 00:28:40.159
score is greater than zero we uh return
00:28:38.360 --> 00:28:42.480
one if the score is less than zero we
00:28:40.159 --> 00:28:44.679
return negative one which is negative
00:28:42.480 --> 00:28:47.240
and otherwise we returns
00:28:44.679 --> 00:28:48.760
zero um we have an accuracy calculation
00:28:47.240 --> 00:28:51.519
function just calculating the outputs
00:28:48.760 --> 00:28:55.840
are good and
00:28:51.519 --> 00:28:57.440
um this is uh the overall label count in
00:28:55.840 --> 00:28:59.919
the in the output so we can see there
00:28:57.440 --> 00:29:03.120
slightly more positives than there are
00:28:59.919 --> 00:29:06.080
negatives and then we can run this and
00:29:03.120 --> 00:29:10.200
we get a a score of
00:29:06.080 --> 00:29:14.760
43 and so one one thing that I have
00:29:10.200 --> 00:29:19.279
found um is I I do a lot of kind
00:29:14.760 --> 00:29:21.240
of research on how to make NLP systems
00:29:19.279 --> 00:29:23.600
better and one of the things I found
00:29:21.240 --> 00:29:26.679
really invaluable
00:29:23.600 --> 00:29:27.840
is if you're in a situation where you
00:29:26.679 --> 00:29:29.720
have a
00:29:27.840 --> 00:29:31.760
set task and you just want to make the
00:29:29.720 --> 00:29:33.760
system better on the set task doing
00:29:31.760 --> 00:29:35.159
comprehensive error analysis and
00:29:33.760 --> 00:29:37.320
understanding where your system is
00:29:35.159 --> 00:29:39.880
failing is one of the best ways to do
00:29:37.320 --> 00:29:42.200
that and I would like to do a very
00:29:39.880 --> 00:29:43.640
rudimentary version of this here and
00:29:42.200 --> 00:29:46.519
what I'm doing essentially is I'm just
00:29:43.640 --> 00:29:47.480
randomly picking uh several examples
00:29:46.519 --> 00:29:49.320
that were
00:29:47.480 --> 00:29:52.000
correct
00:29:49.320 --> 00:29:54.840
um and so like let let's look at the
00:29:52.000 --> 00:29:58.200
examples here um here the true label is
00:29:54.840 --> 00:30:00.760
zero um in this predicted one um it may
00:29:58.200 --> 00:30:03.440
not be as cutting as Woody or as true as
00:30:00.760 --> 00:30:05.039
back in the Glory Days of uh weekend and
00:30:03.440 --> 00:30:07.440
two or three things that I know about
00:30:05.039 --> 00:30:09.640
her but who else engaged in film Mak
00:30:07.440 --> 00:30:12.679
today is so cognizant of the cultural
00:30:09.640 --> 00:30:14.480
and moral issues involved in the process
00:30:12.679 --> 00:30:17.600
so what words in here are a good
00:30:14.480 --> 00:30:20.840
indication that this is a neutral
00:30:17.600 --> 00:30:20.840
sentence any
00:30:23.760 --> 00:30:28.399
ideas little bit tough
00:30:26.240 --> 00:30:30.919
huh starting to think maybe we should be
00:30:28.399 --> 00:30:30.919
using machine
00:30:31.480 --> 00:30:37.440
learning
00:30:34.080 --> 00:30:40.320
um even by the intentionally low
00:30:37.440 --> 00:30:41.559
standards of fratboy humor sority boys
00:30:40.320 --> 00:30:43.840
is a
00:30:41.559 --> 00:30:46.080
Bowser I think frat boy is maybe
00:30:43.840 --> 00:30:47.360
negative sentiment if you're familiar
00:30:46.080 --> 00:30:50.360
with
00:30:47.360 --> 00:30:51.960
us us I don't have any negative
00:30:50.360 --> 00:30:54.519
sentiment but the people who say it that
00:30:51.960 --> 00:30:55.960
way have negative senent maybe so if we
00:30:54.519 --> 00:31:01.080
wanted to go in and do that we could
00:30:55.960 --> 00:31:01.080
maybe I won't save this but
00:31:01.519 --> 00:31:08.919
uh
00:31:04.240 --> 00:31:11.840
um oh whoops I'll go back and fix it uh
00:31:08.919 --> 00:31:14.840
crass crass is pretty obviously negative
00:31:11.840 --> 00:31:14.840
right so I can add
00:31:17.039 --> 00:31:21.080
crass actually let me just add
00:31:21.760 --> 00:31:29.159
CR and then um I'll go back and have our
00:31:26.559 --> 00:31:29.159
train accurate
00:31:32.159 --> 00:31:36.240
wa maybe maybe I need to run the whole
00:31:33.960 --> 00:31:36.240
thing
00:31:36.960 --> 00:31:39.960
again
00:31:40.960 --> 00:31:45.880
and that budg the training accuracy a
00:31:43.679 --> 00:31:50.360
little um the dev test accuracy not very
00:31:45.880 --> 00:31:53.919
much so I could go through and do this
00:31:50.360 --> 00:31:53.919
um let me add
00:31:54.000 --> 00:31:58.320
unengaging so I could go through and do
00:31:56.000 --> 00:32:01.720
this all day and you probably be very
00:31:58.320 --> 00:32:01.720
bored on
00:32:04.240 --> 00:32:08.360
engage but I won't do that uh because we
00:32:06.919 --> 00:32:10.679
have much more important things to be
00:32:08.360 --> 00:32:14.679
doing
00:32:10.679 --> 00:32:16.440
um and uh so anyway we um we could go
00:32:14.679 --> 00:32:18.919
through and design all the features here
00:32:16.440 --> 00:32:21.279
but like why is this complicated like
00:32:18.919 --> 00:32:22.600
the the reason why it was complicated
00:32:21.279 --> 00:32:25.840
became pretty
00:32:22.600 --> 00:32:27.840
clear from the uh from the very
00:32:25.840 --> 00:32:29.639
beginning uh the very first example I
00:32:27.840 --> 00:32:32.200
showed you which was that was a really
00:32:29.639 --> 00:32:34.720
complicated sentence like all of us
00:32:32.200 --> 00:32:36.240
could see that it wasn't like really
00:32:34.720 --> 00:32:38.679
strongly positive it wasn't really
00:32:36.240 --> 00:32:40.519
strongly negative it was kind of like in
00:32:38.679 --> 00:32:42.919
the middle but it was in the middle and
00:32:40.519 --> 00:32:44.600
it said it in a very long way uh you
00:32:42.919 --> 00:32:46.120
know not using any clearly positive
00:32:44.600 --> 00:32:47.639
sentiment words not using any clearly
00:32:46.120 --> 00:32:49.760
negative sentiment
00:32:47.639 --> 00:32:53.760
words
00:32:49.760 --> 00:32:56.519
um so yeah basically I I
00:32:53.760 --> 00:33:00.559
improved um but what are the difficult
00:32:56.519 --> 00:33:03.720
cases uh that we saw here so the first
00:33:00.559 --> 00:33:07.639
one is low frequency
00:33:03.720 --> 00:33:09.760
words so um here's an example the action
00:33:07.639 --> 00:33:11.519
switches between past and present but
00:33:09.760 --> 00:33:13.120
the material link is too tenuous to
00:33:11.519 --> 00:33:16.840
Anchor the emotional connections at
00:33:13.120 --> 00:33:19.519
purport to span a 125 year divide so
00:33:16.840 --> 00:33:21.080
this is negative um tenuous is kind of a
00:33:19.519 --> 00:33:22.799
negative word purport is kind of a
00:33:21.080 --> 00:33:24.760
negative word but it doesn't appear very
00:33:22.799 --> 00:33:26.159
frequently so I would need to spend all
00:33:24.760 --> 00:33:29.720
my time looking for these words and
00:33:26.159 --> 00:33:32.480
trying to them in um here's yet another
00:33:29.720 --> 00:33:34.240
horse franchise mucking up its storyline
00:33:32.480 --> 00:33:36.639
with glitches casual fans could correct
00:33:34.240 --> 00:33:40.159
in their sleep negative
00:33:36.639 --> 00:33:42.600
again um so the solutions here are keep
00:33:40.159 --> 00:33:46.880
working until we get all of them which
00:33:42.600 --> 00:33:49.159
is maybe not super fun um or incorporate
00:33:46.880 --> 00:33:51.639
external resources such as sentiment
00:33:49.159 --> 00:33:52.880
dictionaries that people created uh we
00:33:51.639 --> 00:33:55.960
could do that but that's a lot of
00:33:52.880 --> 00:33:57.480
engineering effort to make something
00:33:55.960 --> 00:34:00.639
work
00:33:57.480 --> 00:34:03.720
um another one is conjugation so we saw
00:34:00.639 --> 00:34:06.600
unengaging I guess that's an example of
00:34:03.720 --> 00:34:08.359
conjugation uh some other ones are
00:34:06.600 --> 00:34:10.520
operatic sprawling picture that's
00:34:08.359 --> 00:34:12.040
entertainingly acted magnificently shot
00:34:10.520 --> 00:34:15.480
and gripping enough to sustain most of
00:34:12.040 --> 00:34:17.399
its 170 minute length so here we have
00:34:15.480 --> 00:34:19.079
magnificently so even if I added
00:34:17.399 --> 00:34:20.480
magnificent this wouldn't have been
00:34:19.079 --> 00:34:23.800
clocked
00:34:20.480 --> 00:34:26.599
right um it's basically an overlong
00:34:23.800 --> 00:34:28.839
episode of tales from the cryp so that's
00:34:26.599 --> 00:34:31.480
maybe another
00:34:28.839 --> 00:34:33.040
example um so some things that we could
00:34:31.480 --> 00:34:35.320
do or what we would have done before the
00:34:33.040 --> 00:34:37.720
modern Paradigm of machine learning is
00:34:35.320 --> 00:34:40.079
we would run some sort of normalizer
00:34:37.720 --> 00:34:42.800
like a stemmer or other things like this
00:34:40.079 --> 00:34:45.240
in order to convert this into uh the
00:34:42.800 --> 00:34:48.599
root wordss that we already have seen
00:34:45.240 --> 00:34:52.040
somewhere in our data or have already
00:34:48.599 --> 00:34:54.040
handed so that requires um conjugation
00:34:52.040 --> 00:34:55.879
analysis or morphological analysis as we
00:34:54.040 --> 00:34:57.400
say it in
00:34:55.879 --> 00:35:00.680
technicals
00:34:57.400 --> 00:35:03.960
negation this is a tricky one so this
00:35:00.680 --> 00:35:06.760
one's not nearly as Dreadful as expected
00:35:03.960 --> 00:35:08.800
so Dreadful is a pretty bad word right
00:35:06.760 --> 00:35:13.000
but not nearly as Dreadful as expected
00:35:08.800 --> 00:35:14.440
is like a solidly neutral um you know or
00:35:13.000 --> 00:35:16.359
maybe even
00:35:14.440 --> 00:35:18.920
positive I would I would say that's
00:35:16.359 --> 00:35:20.640
neutral but you know uh neutral or
00:35:18.920 --> 00:35:23.800
positive it's definitely not
00:35:20.640 --> 00:35:26.359
negative um serving s doesn't serve up a
00:35:23.800 --> 00:35:29.480
whole lot of laughs so laughs is
00:35:26.359 --> 00:35:31.880
obviously positive but not serving UPS
00:35:29.480 --> 00:35:34.440
is obviously
00:35:31.880 --> 00:35:36.839
negative so if negation modifies the
00:35:34.440 --> 00:35:38.240
word disregard it now we would probably
00:35:36.839 --> 00:35:41.440
need to do some sort of syntactic
00:35:38.240 --> 00:35:45.599
analysis or semantic analysis of
00:35:41.440 --> 00:35:47.520
some metaphor an analogy so puts a human
00:35:45.599 --> 00:35:50.640
face on a land most westerners are
00:35:47.520 --> 00:35:52.880
unfamiliar though uh this is
00:35:50.640 --> 00:35:54.960
positive green might want to hang on to
00:35:52.880 --> 00:35:58.800
that ski mask as robbery may be the only
00:35:54.960 --> 00:35:58.800
way to pay for this next project
00:35:58.839 --> 00:36:03.640
so this this is saying that the movie
00:36:01.960 --> 00:36:05.560
was so bad that the director will have
00:36:03.640 --> 00:36:08.359
to rob people in order to get money for
00:36:05.560 --> 00:36:11.000
the next project so that's kind of bad I
00:36:08.359 --> 00:36:12.880
guess um has all the depth of a waiting
00:36:11.000 --> 00:36:14.520
pool this is kind of my favorite one
00:36:12.880 --> 00:36:15.880
because it's really short and sweet but
00:36:14.520 --> 00:36:18.800
you know you need to know how deep a
00:36:15.880 --> 00:36:21.440
waiting pool is um so that's
00:36:18.800 --> 00:36:22.960
negative so the solution here I don't
00:36:21.440 --> 00:36:24.680
really even know how to handle this with
00:36:22.960 --> 00:36:26.880
a rule based system I have no idea how
00:36:24.680 --> 00:36:30.040
we would possibly do this yeah machine
00:36:26.880 --> 00:36:32.400
learning based models seem to be pretty
00:36:30.040 --> 00:36:37.000
adaptive okay and then I start doing
00:36:32.400 --> 00:36:37.000
these ones um anyone have a good
00:36:38.160 --> 00:36:46.800
idea any any other friends who know
00:36:42.520 --> 00:36:50.040
Japanese no okay um so yeah that's
00:36:46.800 --> 00:36:52.839
positive um that one's negative uh and
00:36:50.040 --> 00:36:54.920
the solution here is learn Japanese I
00:36:52.839 --> 00:36:56.800
guess or whatever other language you
00:36:54.920 --> 00:37:00.040
want to process so like obviously
00:36:56.800 --> 00:37:03.720
rule-based systems don't scale very
00:37:00.040 --> 00:37:05.119
well so um we've moved but like rule
00:37:03.720 --> 00:37:06.319
based systems don't scale very well
00:37:05.119 --> 00:37:08.160
we're not going to be using them for
00:37:06.319 --> 00:37:11.400
most of the things we do in this class
00:37:08.160 --> 00:37:14.240
but I do think it's sometimes useful to
00:37:11.400 --> 00:37:15.640
try to create one for your task maybe
00:37:14.240 --> 00:37:16.680
right at the very beginning of a project
00:37:15.640 --> 00:37:18.560
because it gives you an idea about
00:37:16.680 --> 00:37:21.160
what's really hard about the task in
00:37:18.560 --> 00:37:22.480
some cases so um yeah I wouldn't
00:37:21.160 --> 00:37:25.599
entirely discount them I'm not
00:37:22.480 --> 00:37:27.400
introducing them for no reason
00:37:25.599 --> 00:37:29.880
whatsoever
00:37:27.400 --> 00:37:34.160
so next is machine learning based anal
00:37:29.880 --> 00:37:35.400
and machine learning uh in general uh I
00:37:34.160 --> 00:37:36.640
here actually when I say machine
00:37:35.400 --> 00:37:38.160
learning I'm going to be talking about
00:37:36.640 --> 00:37:39.560
the traditional fine-tuning approach
00:37:38.160 --> 00:37:43.520
where we have a training set Dev set
00:37:39.560 --> 00:37:46.359
test set and so we take our training set
00:37:43.520 --> 00:37:49.680
we run some learning algorithm over it
00:37:46.359 --> 00:37:52.319
we have a learned feature extractor F A
00:37:49.680 --> 00:37:55.839
possibly learned feature extractor F
00:37:52.319 --> 00:37:57.880
possibly learned scoring function W and
00:37:55.839 --> 00:38:00.800
uh then we apply our inference algorithm
00:37:57.880 --> 00:38:02.839
our decision Rule and make decisions
00:38:00.800 --> 00:38:04.200
when I say possibly learned actually the
00:38:02.839 --> 00:38:06.119
first example I'm going to give of a
00:38:04.200 --> 00:38:07.760
machine learning based technique is uh
00:38:06.119 --> 00:38:10.079
doesn't have a learned feature extractor
00:38:07.760 --> 00:38:12.800
but most things that we use nowadays do
00:38:10.079 --> 00:38:12.800
have learned feature
00:38:13.200 --> 00:38:18.040
extractors so our first attempt is going
00:38:15.640 --> 00:38:21.760
to be a bag of words model uh and the
00:38:18.040 --> 00:38:27.119
way a bag of wordss model works is uh
00:38:21.760 --> 00:38:30.160
essentially we start out by looking up a
00:38:27.119 --> 00:38:33.240
Vector where one element in the vector
00:38:30.160 --> 00:38:36.240
is uh is one and all the other elements
00:38:33.240 --> 00:38:38.040
in the vector are zero and so if the
00:38:36.240 --> 00:38:40.319
word is different the position in the
00:38:38.040 --> 00:38:42.839
vector that's one will be different we
00:38:40.319 --> 00:38:46.280
add all of these together and this gives
00:38:42.839 --> 00:38:48.200
us a vector where each element is the
00:38:46.280 --> 00:38:50.359
frequency of that word in the vector and
00:38:48.200 --> 00:38:52.520
then we multiply that by weights and we
00:38:50.359 --> 00:38:55.520
get a
00:38:52.520 --> 00:38:57.160
score and um here as I said this is not
00:38:55.520 --> 00:39:00.359
a learned feature
00:38:57.160 --> 00:39:02.079
uh Vector this is basically uh sorry not
00:39:00.359 --> 00:39:04.359
a learn feature extractor this is
00:39:02.079 --> 00:39:06.200
basically a fixed feature extractor but
00:39:04.359 --> 00:39:09.839
the weights themselves are
00:39:06.200 --> 00:39:11.640
learned um so my my question is I
00:39:09.839 --> 00:39:14.599
mentioned a whole lot of problems before
00:39:11.640 --> 00:39:17.480
I mentioned infrequent words I mentioned
00:39:14.599 --> 00:39:20.760
conjugation I mentioned uh different
00:39:17.480 --> 00:39:22.880
languages I mentioned syntax and
00:39:20.760 --> 00:39:24.599
metaphor so which of these do we think
00:39:22.880 --> 00:39:25.440
would be fixed by this sort of learning
00:39:24.599 --> 00:39:27.400
based
00:39:25.440 --> 00:39:29.640
approach
00:39:27.400 --> 00:39:29.640
any
00:39:29.920 --> 00:39:35.200
ideas maybe not fixed maybe made
00:39:32.520 --> 00:39:35.200
significantly
00:39:36.880 --> 00:39:41.560
better any Brave uh brave
00:39:44.880 --> 00:39:48.440
people maybe maybe
00:39:53.720 --> 00:39:58.400
negation okay so maybe doesn't when it
00:39:55.760 --> 00:39:58.400
have a negative qu
00:40:02.960 --> 00:40:07.560
yeah yeah so for the conjugation if we
00:40:05.520 --> 00:40:09.200
had the conjugations of the stems mapped
00:40:07.560 --> 00:40:11.119
in the same position that might fix a
00:40:09.200 --> 00:40:12.920
conjugation problem but I would say if
00:40:11.119 --> 00:40:15.200
you don't do that then this kind of
00:40:12.920 --> 00:40:18.160
fixes conjugation a little bit but maybe
00:40:15.200 --> 00:40:21.319
not not really yeah kind of fix
00:40:18.160 --> 00:40:24.079
conjugation because like they're using
00:40:21.319 --> 00:40:26.760
the same there
00:40:24.079 --> 00:40:28.400
probably different variations so we
00:40:26.760 --> 00:40:31.359
learn how to
00:40:28.400 --> 00:40:33.400
classify surrounding
00:40:31.359 --> 00:40:35.000
structure yeah if it's a big enough
00:40:33.400 --> 00:40:36.760
training set you might have covered the
00:40:35.000 --> 00:40:37.880
various conjugations but if you haven't
00:40:36.760 --> 00:40:43.000
and you don't have any rule-based
00:40:37.880 --> 00:40:43.000
processing it it might still be problems
00:40:45.400 --> 00:40:50.359
yeah yeah so in frequent words if you
00:40:48.280 --> 00:40:52.560
have a large enough training set yeah
00:40:50.359 --> 00:40:54.599
you'll be able to fix it to some extent
00:40:52.560 --> 00:40:56.480
so none of the problems are entirely
00:40:54.599 --> 00:40:57.880
fixed but a lot of them are made better
00:40:56.480 --> 00:40:58.960
different languages is also made better
00:40:57.880 --> 00:41:00.119
if you have training data in that
00:40:58.960 --> 00:41:04.599
language but if you don't then you're
00:41:00.119 --> 00:41:06.240
out of BL so um so now what I'd like to
00:41:04.599 --> 00:41:10.800
do is I'd look to like to look at what
00:41:06.240 --> 00:41:15.079
our vectors represent so basically um in
00:41:10.800 --> 00:41:16.880
uh in binary classification each word um
00:41:15.079 --> 00:41:19.119
sorry so the vectors themselves
00:41:16.880 --> 00:41:21.880
represent the counts of the words here
00:41:19.119 --> 00:41:25.319
I'm talking about what the weight uh
00:41:21.880 --> 00:41:28.520
vectors or matrices correspond to and
00:41:25.319 --> 00:41:31.640
the weight uh Vector here will be
00:41:28.520 --> 00:41:33.680
positive if the word it tends to be
00:41:31.640 --> 00:41:36.680
positive if in a binary classification
00:41:33.680 --> 00:41:38.400
case in a multiclass classification case
00:41:36.680 --> 00:41:42.480
we'll actually have a matrix that looks
00:41:38.400 --> 00:41:45.480
like this where um each column or row uh
00:41:42.480 --> 00:41:47.079
corresponds to the word and each row or
00:41:45.480 --> 00:41:49.319
column corresponds to a label and it
00:41:47.079 --> 00:41:51.960
will be higher if that row tends to uh
00:41:49.319 --> 00:41:54.800
correlate with that uh that word tends
00:41:51.960 --> 00:41:56.920
to correlate that little
00:41:54.800 --> 00:41:59.240
bit so
00:41:56.920 --> 00:42:04.079
this um training of the bag of words
00:41:59.240 --> 00:42:07.720
model is can be done uh so simply that
00:42:04.079 --> 00:42:10.200
we uh can put it in a single slide so
00:42:07.720 --> 00:42:11.599
basically here uh what we do is we start
00:42:10.200 --> 00:42:14.760
out with the feature
00:42:11.599 --> 00:42:18.880
weights and for each example in our data
00:42:14.760 --> 00:42:20.800
set we extract features um the exact way
00:42:18.880 --> 00:42:23.920
I'm extracting features is basically
00:42:20.800 --> 00:42:25.720
splitting uh splitting the words using
00:42:23.920 --> 00:42:28.000
the python split function and then uh
00:42:25.720 --> 00:42:31.319
Counting number of times each word
00:42:28.000 --> 00:42:33.160
exists uh we then run the classifier so
00:42:31.319 --> 00:42:36.280
actually running the classifier is
00:42:33.160 --> 00:42:38.200
exactly the same as what we did for the
00:42:36.280 --> 00:42:42.640
uh the rule based system it's just that
00:42:38.200 --> 00:42:47.359
we have feature vectors instead and
00:42:42.640 --> 00:42:51.559
then if the predicted value is
00:42:47.359 --> 00:42:55.160
not value then for each of the
00:42:51.559 --> 00:42:56.680
features uh in the feature space we
00:42:55.160 --> 00:43:02.200
upweight
00:42:56.680 --> 00:43:03.599
the um we upweight The Weight by the
00:43:02.200 --> 00:43:06.000
vector
00:43:03.599 --> 00:43:09.920
size by or by the amount of the vector
00:43:06.000 --> 00:43:13.240
if Y is positive and we downweight the
00:43:09.920 --> 00:43:16.240
vector uh by the size of the vector if Y
00:43:13.240 --> 00:43:18.520
is negative so this is really really
00:43:16.240 --> 00:43:20.559
simple it's uh probably the simplest
00:43:18.520 --> 00:43:25.079
possible algorithm for training one of
00:43:20.559 --> 00:43:27.559
these models um but I have an
00:43:25.079 --> 00:43:30.040
example in this that you can also take a
00:43:27.559 --> 00:43:31.960
look at here's a trained bag of words
00:43:30.040 --> 00:43:33.680
classifier and we could step through
00:43:31.960 --> 00:43:34.960
this is on exactly the same data set as
00:43:33.680 --> 00:43:37.240
I did before we're training on the
00:43:34.960 --> 00:43:42.359
training set
00:43:37.240 --> 00:43:43.640
um and uh evaluating on the dev set um I
00:43:42.359 --> 00:43:45.880
also have some extra stuff like I'm
00:43:43.640 --> 00:43:47.079
Shuffling the order of the data IDs
00:43:45.880 --> 00:43:49.440
which is really important if you're
00:43:47.079 --> 00:43:53.160
doing this sort of incremental algorithm
00:43:49.440 --> 00:43:54.960
uh because uh what if what if your
00:43:53.160 --> 00:43:57.400
creating data set was ordered in this
00:43:54.960 --> 00:44:00.040
way where you have all of the positive
00:43:57.400 --> 00:44:00.040
labels on
00:44:00.359 --> 00:44:04.520
top and then you have all of the
00:44:02.280 --> 00:44:06.680
negative labels on the
00:44:04.520 --> 00:44:08.200
bottom if you do something like this it
00:44:06.680 --> 00:44:10.200
would see only negative labels at the
00:44:08.200 --> 00:44:11.800
end of training and you might have
00:44:10.200 --> 00:44:14.400
problems because your model would only
00:44:11.800 --> 00:44:17.440
predict negatives so we also Shuffle
00:44:14.400 --> 00:44:20.319
data um and then step through we run the
00:44:17.440 --> 00:44:22.559
classifier and I'm going to run uh five
00:44:20.319 --> 00:44:23.640
epochs of training through the data set
00:44:22.559 --> 00:44:27.160
uh very
00:44:23.640 --> 00:44:29.599
fast and calculate our accuracy
00:44:27.160 --> 00:44:33.280
and this got 75% accuracy on the
00:44:29.599 --> 00:44:36.160
training data set and uh 56% accuracy on
00:44:33.280 --> 00:44:40.000
the Deb data set so uh if you remember
00:44:36.160 --> 00:44:41.520
our rule-based classifier had 42 uh 42
00:44:40.000 --> 00:44:43.880
accuracy and now our training based
00:44:41.520 --> 00:44:45.760
classifier has 56 accuracy but it's
00:44:43.880 --> 00:44:49.359
overfitting heavily to the training side
00:44:45.760 --> 00:44:50.880
so um basically this is a pretty strong
00:44:49.359 --> 00:44:53.480
advertisement for why we should be using
00:44:50.880 --> 00:44:54.960
machine learning you know I the amount
00:44:53.480 --> 00:44:57.800
of code that we had for this machine
00:44:54.960 --> 00:44:59.720
learning model is basically very similar
00:44:57.800 --> 00:45:02.680
um it's not using any external libraries
00:44:59.720 --> 00:45:02.680
but we're getting better at
00:45:03.599 --> 00:45:08.800
this
00:45:05.800 --> 00:45:08.800
cool
00:45:09.559 --> 00:45:16.000
so cool any any questions
00:45:13.520 --> 00:45:18.240
here and so I'm going to talk about the
00:45:16.000 --> 00:45:20.760
connection to between this algorithm and
00:45:18.240 --> 00:45:22.839
neural networks in the next class um
00:45:20.760 --> 00:45:24.200
because this actually is using a very
00:45:22.839 --> 00:45:26.319
similar training algorithm to what we
00:45:24.200 --> 00:45:27.480
use in neural networks with some uh
00:45:26.319 --> 00:45:30.079
particular
00:45:27.480 --> 00:45:32.839
assumptions cool um so what's missing in
00:45:30.079 --> 00:45:34.800
bag of words um still handling of
00:45:32.839 --> 00:45:36.880
conjugation or compound words is not
00:45:34.800 --> 00:45:39.160
perfect it we can do it to some extent
00:45:36.880 --> 00:45:41.079
to the point where we can uh memorize
00:45:39.160 --> 00:45:44.079
things so I love this movie I love this
00:45:41.079 --> 00:45:46.920
movie another thing is handling word Ser
00:45:44.079 --> 00:45:49.240
uh similarities so I love this movie and
00:45:46.920 --> 00:45:50.720
I adore this movie uh these basically
00:45:49.240 --> 00:45:52.119
mean the same thing as humans we know
00:45:50.720 --> 00:45:54.200
they mean the same thing so we should be
00:45:52.119 --> 00:45:56.079
able to take advantage of that fact to
00:45:54.200 --> 00:45:57.839
learn better models but we're not doing
00:45:56.079 --> 00:46:02.760
that in this model at the moment because
00:45:57.839 --> 00:46:05.440
each unit is uh treated as a atomic unit
00:46:02.760 --> 00:46:08.040
and there's no idea of
00:46:05.440 --> 00:46:11.040
similarity also handling of combination
00:46:08.040 --> 00:46:12.760
features so um I love this movie and I
00:46:11.040 --> 00:46:14.920
don't love this movie I hate this movie
00:46:12.760 --> 00:46:17.079
and I don't hate this movie actually
00:46:14.920 --> 00:46:20.400
this is a little bit tricky because
00:46:17.079 --> 00:46:23.240
negative words are slightly indicative
00:46:20.400 --> 00:46:25.280
of it being negative but actually what
00:46:23.240 --> 00:46:28.119
they do is they negate the other things
00:46:25.280 --> 00:46:28.119
that you're saying in the
00:46:28.240 --> 00:46:36.559
sentence
00:46:30.720 --> 00:46:40.480
so um like love is positive hate is
00:46:36.559 --> 00:46:40.480
negative but like don't
00:46:50.359 --> 00:46:56.079
love it's actually kind of like this
00:46:52.839 --> 00:46:59.359
right like um Love is very positive POS
00:46:56.079 --> 00:47:01.760
hate is very negative but don't love is
00:46:59.359 --> 00:47:04.680
like slightly less positive than don't
00:47:01.760 --> 00:47:06.160
hate right so um It's actually kind of
00:47:04.680 --> 00:47:07.559
tricky because you need to combine them
00:47:06.160 --> 00:47:10.720
together and figure out what's going on
00:47:07.559 --> 00:47:12.280
based on that another example that a lot
00:47:10.720 --> 00:47:14.160
of people might not think of immediately
00:47:12.280 --> 00:47:17.880
but is super super common in sentiment
00:47:14.160 --> 00:47:20.160
analysis or any other thing is butt so
00:47:17.880 --> 00:47:22.599
basically what but does is it throws
00:47:20.160 --> 00:47:24.160
away all the stuff that you said before
00:47:22.599 --> 00:47:26.119
um and you can just pay attention to the
00:47:24.160 --> 00:47:29.000
stuff that you saw beforehand so like we
00:47:26.119 --> 00:47:30.440
could even add this to our um like if
00:47:29.000 --> 00:47:31.760
you want to add this to your rule based
00:47:30.440 --> 00:47:33.240
classifier you can do that you just
00:47:31.760 --> 00:47:34.640
search for butt and delete everything
00:47:33.240 --> 00:47:37.240
before it and see if that inputs your
00:47:34.640 --> 00:47:39.240
accuracy might be might be a fun very
00:47:37.240 --> 00:47:43.480
quick thing
00:47:39.240 --> 00:47:44.880
to cool so the better solution which is
00:47:43.480 --> 00:47:46.800
what we're going to talk about for every
00:47:44.880 --> 00:47:49.480
other class other than uh other than
00:47:46.800 --> 00:47:52.160
this one is neural network models and
00:47:49.480 --> 00:47:55.800
basically uh what they do is they do a
00:47:52.160 --> 00:47:59.400
lookup of uh dense word embeddings so
00:47:55.800 --> 00:48:02.520
instead of looking up uh individual uh
00:47:59.400 --> 00:48:04.640
sparse uh vectors individual one hot
00:48:02.520 --> 00:48:06.920
vectors they look up dense word
00:48:04.640 --> 00:48:09.680
embeddings and then throw them into some
00:48:06.920 --> 00:48:11.880
complicated function to extract features
00:48:09.680 --> 00:48:16.359
and based on the features uh multiply by
00:48:11.880 --> 00:48:18.280
weights and get a score um and if you're
00:48:16.359 --> 00:48:20.359
doing text classification in the
00:48:18.280 --> 00:48:22.520
traditional way this is normally what
00:48:20.359 --> 00:48:23.760
you do um if you're doing text
00:48:22.520 --> 00:48:25.960
classification with something like
00:48:23.760 --> 00:48:27.280
prompting you're still actually doing
00:48:25.960 --> 00:48:29.960
this because you're calculating the
00:48:27.280 --> 00:48:32.960
score of the next word to predict and
00:48:29.960 --> 00:48:34.720
that's done in exactly the same way so
00:48:32.960 --> 00:48:37.760
uh even if you're using a large language
00:48:34.720 --> 00:48:39.359
model like GPT this is still probably
00:48:37.760 --> 00:48:41.800
happening under the hood unless open the
00:48:39.359 --> 00:48:43.400
eye invented something that very
00:48:41.800 --> 00:48:45.559
different in Alien than anything else
00:48:43.400 --> 00:48:48.440
that we know of but I I'm guessing that
00:48:45.559 --> 00:48:48.440
that propably hasn't
00:48:48.480 --> 00:48:52.880
happen um one nice thing about neural
00:48:50.880 --> 00:48:54.480
networks is neural networks
00:48:52.880 --> 00:48:57.559
theoretically are powerful enough to
00:48:54.480 --> 00:49:00.000
solve any task if you make them uh deep
00:48:57.559 --> 00:49:01.160
enough or wide enough uh like if you
00:49:00.000 --> 00:49:04.520
make them wide enough and then if you
00:49:01.160 --> 00:49:06.799
make them deep it also helps further so
00:49:04.520 --> 00:49:08.079
anytime somebody says well you can't
00:49:06.799 --> 00:49:11.119
just solve that problem with neural
00:49:08.079 --> 00:49:13.240
networks you know that they're lying
00:49:11.119 --> 00:49:15.720
basically because they theoretically can
00:49:13.240 --> 00:49:17.359
solve every problem uh but you have you
00:49:15.720 --> 00:49:19.799
have issues of data you have issues of
00:49:17.359 --> 00:49:23.079
other things like that so you know they
00:49:19.799 --> 00:49:23.079
don't just necessarily work
00:49:23.119 --> 00:49:28.040
outs cool um so the final thing I'd like
00:49:26.400 --> 00:49:29.319
to talk about is the road map going
00:49:28.040 --> 00:49:31.319
forward some of the things I'm going to
00:49:29.319 --> 00:49:32.799
cover in the class and some of the
00:49:31.319 --> 00:49:35.200
logistics
00:49:32.799 --> 00:49:36.799
issues so um the first thing I'm going
00:49:35.200 --> 00:49:38.240
to talk about in the class is language
00:49:36.799 --> 00:49:40.559
modeling fun
00:49:38.240 --> 00:49:42.720
fundamentals and uh so this could
00:49:40.559 --> 00:49:44.240
include language models uh that just
00:49:42.720 --> 00:49:46.559
predict the next words it could include
00:49:44.240 --> 00:49:50.559
language models that predict the output
00:49:46.559 --> 00:49:51.599
given the uh the input or the prompt um
00:49:50.559 --> 00:49:54.559
I'm going to be talking about
00:49:51.599 --> 00:49:56.520
representing words uh how how we get
00:49:54.559 --> 00:49:59.319
word representation subword models other
00:49:56.520 --> 00:50:01.440
things like that uh then go kind of
00:49:59.319 --> 00:50:04.200
deeper into language modeling uh how do
00:50:01.440 --> 00:50:07.799
we do it how do we evaluate it other
00:50:04.200 --> 00:50:10.920
things um sequence encoding uh and this
00:50:07.799 --> 00:50:13.240
is going to cover things like uh
00:50:10.920 --> 00:50:16.280
Transformers uh self attention modals
00:50:13.240 --> 00:50:18.559
but also very quickly cnns and rnns
00:50:16.280 --> 00:50:20.880
which are useful in some
00:50:18.559 --> 00:50:22.200
cases um and then we're going to
00:50:20.880 --> 00:50:24.040
specifically go very deep into the
00:50:22.200 --> 00:50:25.960
Transformer architecture and also talk a
00:50:24.040 --> 00:50:27.280
little bit about some of the modern uh
00:50:25.960 --> 00:50:30.240
improvements to the Transformer
00:50:27.280 --> 00:50:31.839
architecture so the Transformer we're
00:50:30.240 --> 00:50:33.839
using nowadays is very different than
00:50:31.839 --> 00:50:36.200
the Transformer that was invented in
00:50:33.839 --> 00:50:37.240
2017 uh so we're going to talk well I
00:50:36.200 --> 00:50:38.760
wouldn't say very different but
00:50:37.240 --> 00:50:41.359
different enough that it's important so
00:50:38.760 --> 00:50:43.280
we're going to talk about some of those
00:50:41.359 --> 00:50:45.079
things second thing I'd like to talk
00:50:43.280 --> 00:50:47.000
about is training and inference methods
00:50:45.079 --> 00:50:48.839
so this includes uh generation
00:50:47.000 --> 00:50:52.119
algorithms uh so we're going to have a
00:50:48.839 --> 00:50:55.520
whole class on how we generate text uh
00:50:52.119 --> 00:50:58.319
in different ways uh prompting how uh we
00:50:55.520 --> 00:50:59.720
can prompt things I hear uh world class
00:50:58.319 --> 00:51:01.799
prompt engineers make a lot of money
00:50:59.720 --> 00:51:05.480
nowadays so uh you'll want to pay
00:51:01.799 --> 00:51:08.760
attention to that one um and instruction
00:51:05.480 --> 00:51:11.520
tuning uh so how do we train models to
00:51:08.760 --> 00:51:13.720
handle a lot of different tasks and
00:51:11.520 --> 00:51:15.839
reinforcement learning so how do we uh
00:51:13.720 --> 00:51:18.520
you know like actually generate outputs
00:51:15.839 --> 00:51:19.839
uh kind of Judge them and then learn
00:51:18.520 --> 00:51:22.599
from
00:51:19.839 --> 00:51:25.880
there also experimental design and
00:51:22.599 --> 00:51:28.079
evaluation so experimental design uh so
00:51:25.880 --> 00:51:30.480
how do we design an experiment well uh
00:51:28.079 --> 00:51:32.000
so that it backs up what we want to be
00:51:30.480 --> 00:51:34.559
uh our conclusions that we want to be
00:51:32.000 --> 00:51:37.000
backing up how do we do human annotation
00:51:34.559 --> 00:51:38.880
of data in a reliable way this is
00:51:37.000 --> 00:51:41.160
getting harder and harder as models get
00:51:38.880 --> 00:51:43.359
better and better because uh getting
00:51:41.160 --> 00:51:45.000
humans who don't care very much about
00:51:43.359 --> 00:51:48.559
The annotation task they might do worse
00:51:45.000 --> 00:51:51.119
than gp4 so um you need to be careful of
00:51:48.559 --> 00:51:52.240
that also debugging and interpretation
00:51:51.119 --> 00:51:53.960
technique so what are some of the
00:51:52.240 --> 00:51:55.160
automatic techniques that you can do to
00:51:53.960 --> 00:51:57.720
quickly figure out what's going wrong
00:51:55.160 --> 00:52:00.040
with your models and improve
00:51:57.720 --> 00:52:01.599
them and uh bias and fairness
00:52:00.040 --> 00:52:04.200
considerations so it's really really
00:52:01.599 --> 00:52:05.799
important nowadays uh that models are
00:52:04.200 --> 00:52:07.880
being deployed to real people in the
00:52:05.799 --> 00:52:09.880
real world and like actually causing
00:52:07.880 --> 00:52:11.760
harm to people in some cases that we
00:52:09.880 --> 00:52:15.160
need to be worried about
00:52:11.760 --> 00:52:17.000
that Advanced Training in architectures
00:52:15.160 --> 00:52:19.280
so we're going to talk about distill
00:52:17.000 --> 00:52:21.400
distillation and quantization how can we
00:52:19.280 --> 00:52:23.520
make small language models uh that
00:52:21.400 --> 00:52:24.880
actually still work well like not large
00:52:23.520 --> 00:52:27.559
you can run them on your phone you can
00:52:24.880 --> 00:52:29.920
run them on your local
00:52:27.559 --> 00:52:31.640
laptop um ensembling and mixtures of
00:52:29.920 --> 00:52:33.480
experts how can we combine together
00:52:31.640 --> 00:52:34.760
multiple models in order to create
00:52:33.480 --> 00:52:35.880
models that are better than the sum of
00:52:34.760 --> 00:52:38.799
their
00:52:35.880 --> 00:52:40.720
parts and um retrieval and retrieval
00:52:38.799 --> 00:52:43.920
augmented
00:52:40.720 --> 00:52:45.480
generation long sequence models uh so
00:52:43.920 --> 00:52:49.920
how do we handle long
00:52:45.480 --> 00:52:52.240
outputs um and uh we're going to talk
00:52:49.920 --> 00:52:55.760
about applications to complex reasoning
00:52:52.240 --> 00:52:57.760
tasks code generation language agents
00:52:55.760 --> 00:52:59.920
and knowledge-based QA and information
00:52:57.760 --> 00:53:04.160
extraction I picked
00:52:59.920 --> 00:53:06.760
these because they seem to be maybe the
00:53:04.160 --> 00:53:09.880
most important at least in research
00:53:06.760 --> 00:53:11.440
nowadays and also they cover uh the
00:53:09.880 --> 00:53:13.640
things that when I talk to people in
00:53:11.440 --> 00:53:15.280
Industry are kind of most interested in
00:53:13.640 --> 00:53:17.559
so hopefully it'll be useful regardless
00:53:15.280 --> 00:53:19.799
of uh whether you plan on doing research
00:53:17.559 --> 00:53:22.839
or or plan on doing industry related
00:53:19.799 --> 00:53:24.160
things uh by by the way the two things
00:53:22.839 --> 00:53:25.920
that when I talk to people in Industry
00:53:24.160 --> 00:53:29.599
they're most interested in are Rag and
00:53:25.920 --> 00:53:31.079
code generation at the moment for now um
00:53:29.599 --> 00:53:32.319
so those are ones that you'll want to
00:53:31.079 --> 00:53:34.680
pay attention
00:53:32.319 --> 00:53:36.599
to and then finally we have a few
00:53:34.680 --> 00:53:40.079
lectures on Linguistics and
00:53:36.599 --> 00:53:42.720
multilinguality um I love Linguistics
00:53:40.079 --> 00:53:44.839
but uh to be honest at the moment most
00:53:42.720 --> 00:53:47.760
of our Cutting Edge models don't
00:53:44.839 --> 00:53:49.240
explicitly use linguistic structure um
00:53:47.760 --> 00:53:50.799
but I still think it's useful to know
00:53:49.240 --> 00:53:52.760
about it especially if you're working on
00:53:50.799 --> 00:53:54.880
multilingual things especially if you're
00:53:52.760 --> 00:53:57.040
interested in very robust generalization
00:53:54.880 --> 00:53:58.920
to new models so we're going to talk a
00:53:57.040 --> 00:54:02.599
little bit about that and also
00:53:58.920 --> 00:54:06.079
multilingual LP I'm going to have
00:54:02.599 --> 00:54:09.119
fure so also if you have any suggestions
00:54:06.079 --> 00:54:11.400
um we have two guest lecture slots still
00:54:09.119 --> 00:54:12.799
open uh that I'm trying to fill so if
00:54:11.400 --> 00:54:15.440
you have any things that you really want
00:54:12.799 --> 00:54:16.440
to hear about um I could either add them
00:54:15.440 --> 00:54:19.319
to the
00:54:16.440 --> 00:54:21.079
existing you know content or I could
00:54:19.319 --> 00:54:23.240
invite a guest lecturer who's working on
00:54:21.079 --> 00:54:24.079
that topic so you know please feel free
00:54:23.240 --> 00:54:26.760
to tell
00:54:24.079 --> 00:54:29.160
me um then the class format and
00:54:26.760 --> 00:54:32.280
structure uh the class
00:54:29.160 --> 00:54:34.000
content my goal is to learn in detail
00:54:32.280 --> 00:54:36.640
about building NLP systems from a
00:54:34.000 --> 00:54:40.520
research perspective so this is a 700
00:54:36.640 --> 00:54:43.599
level course so it's aiming to be for
00:54:40.520 --> 00:54:46.960
people who really want to try new and
00:54:43.599 --> 00:54:49.280
Innovative things in uh kind of natural
00:54:46.960 --> 00:54:51.359
language processing it's not going to
00:54:49.280 --> 00:54:52.760
focus solely on reimplementing things
00:54:51.359 --> 00:54:54.319
that have been done before including in
00:54:52.760 --> 00:54:55.280
the project I'm going to be expecting
00:54:54.319 --> 00:54:58.480
everybody to do something something
00:54:55.280 --> 00:54:59.920
that's kind of new whether it's coming
00:54:58.480 --> 00:55:01.359
up with a new method or applying
00:54:59.920 --> 00:55:03.559
existing methods to a place where they
00:55:01.359 --> 00:55:05.079
haven't been used before or building out
00:55:03.559 --> 00:55:06.640
things for a new language or something
00:55:05.079 --> 00:55:08.359
like that so that's kind of one of the
00:55:06.640 --> 00:55:11.480
major goals of this
00:55:08.359 --> 00:55:13.000
class um learn basic and advanced topics
00:55:11.480 --> 00:55:15.559
in machine learning approaches to NLP
00:55:13.000 --> 00:55:18.359
and language models learn some basic
00:55:15.559 --> 00:55:21.480
linguistic knowledge useful in NLP uh
00:55:18.359 --> 00:55:23.200
see case studies of NLP applications and
00:55:21.480 --> 00:55:25.680
learn how to identify unique problems
00:55:23.200 --> 00:55:29.039
for each um one thing i' like to point
00:55:25.680 --> 00:55:31.160
out is I'm not going to cover every NLP
00:55:29.039 --> 00:55:32.920
application ever because that would be
00:55:31.160 --> 00:55:35.520
absolutely impossible NLP is being used
00:55:32.920 --> 00:55:37.079
in so many different areas nowadays but
00:55:35.520 --> 00:55:38.960
what I want people to pay attention to
00:55:37.079 --> 00:55:41.280
like even if you're not super interested
00:55:38.960 --> 00:55:42.400
in code generation for example what you
00:55:41.280 --> 00:55:44.200
can do is you can look at code
00:55:42.400 --> 00:55:46.160
generation look at how people identify
00:55:44.200 --> 00:55:47.680
problems look at the methods that people
00:55:46.160 --> 00:55:50.880
have proposed to solve those unique
00:55:47.680 --> 00:55:53.039
problems and then kind of map that try
00:55:50.880 --> 00:55:54.799
to do some generalization onto your own
00:55:53.039 --> 00:55:57.799
problems of Interest so uh that's kind
00:55:54.799 --> 00:56:00.280
of the goal of the NLP
00:55:57.799 --> 00:56:02.440
applications finally uh learning how to
00:56:00.280 --> 00:56:05.160
debug when and where NLP systems fail
00:56:02.440 --> 00:56:08.200
and build improvements based on this so
00:56:05.160 --> 00:56:10.200
um ever since I was a graduate student
00:56:08.200 --> 00:56:12.720
this has been like one of the really
00:56:10.200 --> 00:56:15.920
important things that I feel like I've
00:56:12.720 --> 00:56:17.440
done well or done better than some other
00:56:15.920 --> 00:56:19.280
people and I I feel like it's a really
00:56:17.440 --> 00:56:21.119
good way to like even if you're only
00:56:19.280 --> 00:56:22.680
interested in improving accuracy knowing
00:56:21.119 --> 00:56:25.039
why your system's failing still is the
00:56:22.680 --> 00:56:27.599
best way to do that I so I'm going to
00:56:25.039 --> 00:56:30.559
put a lot of emphasis on
00:56:27.599 --> 00:56:32.559
that in terms of the class format um
00:56:30.559 --> 00:56:36.280
before class for some classes there are
00:56:32.559 --> 00:56:37.880
recommended reading uh this can be
00:56:36.280 --> 00:56:39.559
helpful to read I'm never going to
00:56:37.880 --> 00:56:41.119
expect you to definitely have read it
00:56:39.559 --> 00:56:42.480
before the class but I would suggest
00:56:41.119 --> 00:56:45.160
that maybe you'll get more out of the
00:56:42.480 --> 00:56:47.319
class if you do that um during class
00:56:45.160 --> 00:56:48.079
we'll have the lecture um in discussion
00:56:47.319 --> 00:56:50.559
with
00:56:48.079 --> 00:56:52.359
everybody um sometimes we'll have a code
00:56:50.559 --> 00:56:55.839
or data walk
00:56:52.359 --> 00:56:58.760
um actually this is a a little bit old I
00:56:55.839 --> 00:57:01.880
I have this slide we're this year we're
00:56:58.760 --> 00:57:04.160
going to be adding more uh code and data
00:57:01.880 --> 00:57:07.400
walks during office hours and the way it
00:57:04.160 --> 00:57:09.400
will work is one of the Tas we have
00:57:07.400 --> 00:57:11.160
seven Tas who I'm going to introduce
00:57:09.400 --> 00:57:15.000
very soon but one of the Tas will be
00:57:11.160 --> 00:57:16.839
doing this kind of recitation where you
00:57:15.000 --> 00:57:18.200
um where we go over a library so if
00:57:16.839 --> 00:57:19.480
you're not familiar with the library and
00:57:18.200 --> 00:57:21.960
you want to be more familiar with the
00:57:19.480 --> 00:57:23.720
library you can join this and uh then
00:57:21.960 --> 00:57:25.400
we'll be able to do this and this will
00:57:23.720 --> 00:57:28.240
cover things like
00:57:25.400 --> 00:57:31.039
um pie torch and sentence piece uh we're
00:57:28.240 --> 00:57:33.280
going to start out with hugging face um
00:57:31.039 --> 00:57:36.559
inference stuff like
00:57:33.280 --> 00:57:41.520
VM uh debugging software like
00:57:36.559 --> 00:57:41.520
Xeno um what were the other
00:57:41.960 --> 00:57:47.200
ones oh the open AI API and light llm
00:57:45.680 --> 00:57:50.520
other stuff like that so we we have lots
00:57:47.200 --> 00:57:53.599
of them planned we'll uh uh we'll update
00:57:50.520 --> 00:57:54.839
that um and then after class after
00:57:53.599 --> 00:57:58.079
almost every class we'll have a question
00:57:54.839 --> 00:58:00.079
quiz um and the quiz is intended to just
00:57:58.079 --> 00:58:02.000
you know make sure that you uh paid
00:58:00.079 --> 00:58:04.480
attention to the material and are able
00:58:02.000 --> 00:58:07.520
to answer questions about it we will aim
00:58:04.480 --> 00:58:09.559
to release it on the day of the course
00:58:07.520 --> 00:58:11.599
the day of the actual lecture and it
00:58:09.559 --> 00:58:14.559
will be due at the end of the following
00:58:11.599 --> 00:58:15.960
day of the lecture so um it will be
00:58:14.559 --> 00:58:18.920
three questions it probably shouldn't
00:58:15.960 --> 00:58:20.680
take a whole lot of time but um uh yeah
00:58:18.920 --> 00:58:23.400
so we'll H
00:58:20.680 --> 00:58:26.319
that in terms of assignments assignment
00:58:23.400 --> 00:58:28.640
one is going to be build your own llama
00:58:26.319 --> 00:58:30.200
and so what this is going to look like
00:58:28.640 --> 00:58:32.680
is we're going to give you a partial
00:58:30.200 --> 00:58:34.319
implementation of llama which is kind of
00:58:32.680 --> 00:58:37.960
the most popular open source language
00:58:34.319 --> 00:58:40.160
model nowadays and ask you to fill in um
00:58:37.960 --> 00:58:42.839
ask you to fill in the parts we're going
00:58:40.160 --> 00:58:45.920
to train a very small version of llama
00:58:42.839 --> 00:58:47.319
on a small data set and get it to work
00:58:45.920 --> 00:58:48.880
and the reason why it's very small is
00:58:47.319 --> 00:58:50.480
because the smallest actual version of
00:58:48.880 --> 00:58:53.039
llama is 7 billion
00:58:50.480 --> 00:58:55.359
parameters um and that might be a little
00:58:53.039 --> 00:58:58.400
bit difficult to train with
00:58:55.359 --> 00:59:00.680
resources um for assignment two we're
00:58:58.400 --> 00:59:04.559
going to try to do an NLP task from
00:59:00.680 --> 00:59:06.920
scratch and so the way this will work is
00:59:04.559 --> 00:59:08.520
we're going to give you an assignment
00:59:06.920 --> 00:59:10.880
which we're not going to give you an
00:59:08.520 --> 00:59:13.400
actual data set and instead we're going
00:59:10.880 --> 00:59:15.760
to ask you to uh perform data creation
00:59:13.400 --> 00:59:19.359
modeling and evaluation for a specified
00:59:15.760 --> 00:59:20.640
task and so we're going to tell you uh
00:59:19.359 --> 00:59:22.599
what to do but we're not going to tell
00:59:20.640 --> 00:59:26.400
you exactly how to do it but we're going
00:59:22.599 --> 00:59:29.680
to try to give as conrete directions as
00:59:26.400 --> 00:59:32.359
we can um
00:59:29.680 --> 00:59:34.160
yeah will you be given a parameter limit
00:59:32.359 --> 00:59:36.559
on the model so that's a good question
00:59:34.160 --> 00:59:39.119
or like a expense limit or something
00:59:36.559 --> 00:59:40.440
like that um I maybe actually I should
00:59:39.119 --> 00:59:44.240
take a break from the assignments and
00:59:40.440 --> 00:59:46.520
talk about compute so right now um for
00:59:44.240 --> 00:59:49.319
assignment one we're planning on having
00:59:46.520 --> 00:59:51.599
this be able to be done either on a Mac
00:59:49.319 --> 00:59:53.520
laptop with an M1 or M2 processor which
00:59:51.599 --> 00:59:57.079
I think a lot of people have or Google
00:59:53.520 --> 00:59:59.839
collab um so it should be like
00:59:57.079 --> 01:00:02.160
sufficient to use free computational
00:59:59.839 --> 01:00:03.640
resources that you have for number two
01:00:02.160 --> 01:00:06.079
we'll think about that I think that's
01:00:03.640 --> 01:00:08.280
important we do have Google cloud
01:00:06.079 --> 01:00:11.520
credits for $50 for everybody and I'm
01:00:08.280 --> 01:00:13.440
working to get AWS credits for more um
01:00:11.520 --> 01:00:18.160
but the cloud providers nowadays are
01:00:13.440 --> 01:00:19.680
being very stingy so um so it's uh been
01:00:18.160 --> 01:00:22.160
a little bit of a fight to get uh
01:00:19.680 --> 01:00:23.680
credits but I I it is very important so
01:00:22.160 --> 01:00:28.480
I'm going to try to get as as many as we
01:00:23.680 --> 01:00:31.119
can um and so yeah I I think basically
01:00:28.480 --> 01:00:32.280
uh there will be some sort of like limit
01:00:31.119 --> 01:00:34.480
on the amount of things you can
01:00:32.280 --> 01:00:36.240
practically do and so because of that
01:00:34.480 --> 01:00:39.920
I'm hoping that people will rely very
01:00:36.240 --> 01:00:43.359
heavily on pre-trained models um or uh
01:00:39.920 --> 01:00:46.079
yeah pre-trained models
01:00:43.359 --> 01:00:49.599
and yeah so that that's the the short
01:00:46.079 --> 01:00:52.799
story B um the second thing uh the
01:00:49.599 --> 01:00:54.720
assignment three is to do a survey of
01:00:52.799 --> 01:00:57.920
some sort of state-ofthe-art research
01:00:54.720 --> 01:01:00.760
resarch and do a reimplementation of
01:00:57.920 --> 01:01:02.000
this and in doing this again you will
01:01:00.760 --> 01:01:03.440
have to think about something that's
01:01:02.000 --> 01:01:06.359
feasible within computational
01:01:03.440 --> 01:01:08.680
constraints um and so you can discuss
01:01:06.359 --> 01:01:11.839
with your Tas about uh about the best
01:01:08.680 --> 01:01:13.920
way to do this um and then the final
01:01:11.839 --> 01:01:15.400
project is to perform a unique project
01:01:13.920 --> 01:01:17.559
that either improves on the state-of-the
01:01:15.400 --> 01:01:21.000
art with respect to whatever you would
01:01:17.559 --> 01:01:23.440
like to improve with this could be uh
01:01:21.000 --> 01:01:25.280
accuracy for sure this could be
01:01:23.440 --> 01:01:27.760
efficiency
01:01:25.280 --> 01:01:29.599
it could be some sense of
01:01:27.760 --> 01:01:31.520
interpretability but if it's going to be
01:01:29.599 --> 01:01:33.599
something like interpretability you'll
01:01:31.520 --> 01:01:35.440
have to discuss with us what that means
01:01:33.599 --> 01:01:37.240
like how we measure that how we can like
01:01:35.440 --> 01:01:40.839
actually say that you did a good job
01:01:37.240 --> 01:01:42.839
with improving that um another thing
01:01:40.839 --> 01:01:44.680
that you can do is take whatever you
01:01:42.839 --> 01:01:47.280
implemented for assignment 3 and apply
01:01:44.680 --> 01:01:49.039
it to a new task or apply it to a new
01:01:47.280 --> 01:01:50.760
language that has never been examined
01:01:49.039 --> 01:01:53.119
before so these are also acceptable
01:01:50.760 --> 01:01:54.240
final projects but basically the idea is
01:01:53.119 --> 01:01:55.559
for the final project you need to do
01:01:54.240 --> 01:01:57.480
something something new that hasn't been
01:01:55.559 --> 01:01:59.880
done before and create new knowledge
01:01:57.480 --> 01:02:04.520
with the respect
01:01:59.880 --> 01:02:07.640
toy um so for this the instructor is me
01:02:04.520 --> 01:02:09.920
um I'm uh looking forward to you know
01:02:07.640 --> 01:02:13.599
discussing and working with all of you
01:02:09.920 --> 01:02:16.119
um for TAS we have seven Tas uh two of
01:02:13.599 --> 01:02:18.319
them are in transit so they're not here
01:02:16.119 --> 01:02:22.279
today um the other ones uh Tas would you
01:02:18.319 --> 01:02:22.279
mind coming up uh to introduce
01:02:23.359 --> 01:02:26.359
yourself
01:02:28.400 --> 01:02:32.839
so um yeah nhir and akshai couldn't be
01:02:31.599 --> 01:02:34.039
here today because they're traveling
01:02:32.839 --> 01:02:37.119
I'll introduce them later because
01:02:34.039 --> 01:02:37.119
they're coming uh next
01:02:40.359 --> 01:02:46.480
time cool and what I'd like everybody to
01:02:43.000 --> 01:02:48.680
do is say um like you know what your
01:02:46.480 --> 01:02:53.079
name is uh what
01:02:48.680 --> 01:02:55.799
your like maybe what you're interested
01:02:53.079 --> 01:02:57.319
in um and the reason the goal of this is
01:02:55.799 --> 01:02:59.200
number one for everybody to know who you
01:02:57.319 --> 01:03:00.720
are and number two for everybody to know
01:02:59.200 --> 01:03:03.440
who the best person to talk to is if
01:03:00.720 --> 01:03:03.440
they're interested in
01:03:04.200 --> 01:03:09.079
particular hi uh I'm
01:03:07.000 --> 01:03:15.520
Aila second
01:03:09.079 --> 01:03:15.520
year I work on language and social
01:03:16.200 --> 01:03:24.559
and I'm I'm a second this year PhD
01:03:21.160 --> 01:03:26.799
student Grand and Shar with you I search
01:03:24.559 --> 01:03:28.480
is like started in the border of MP and
01:03:26.799 --> 01:03:31.000
computer interaction with a lot of work
01:03:28.480 --> 01:03:32.640
on automating parts of the developer
01:03:31.000 --> 01:03:35.319
experience to make it easier for anyone
01:03:32.640 --> 01:03:35.319
to
01:03:39.090 --> 01:03:42.179
[Music]
01:03:47.520 --> 01:03:53.279
orif
01:03:50.079 --> 01:03:54.680
everyone first
01:03:53.279 --> 01:03:57.119
year
01:03:54.680 --> 01:04:00.119
[Music]
01:03:57.119 --> 01:04:03.559
I don't like updating primar models I
01:04:00.119 --> 01:04:03.559
hope to not update Prim
01:04:14.599 --> 01:04:19.400
modelm yeah thanks a lot everyone and
01:04:17.200 --> 01:04:19.400
yeah
01:04:20.839 --> 01:04:29.400
than and so we will um we'll have people
01:04:25.640 --> 01:04:30.799
uh kind of have office hours uh every ta
01:04:29.400 --> 01:04:32.880
has office hours at a regular time
01:04:30.799 --> 01:04:34.480
during the week uh please feel free to
01:04:32.880 --> 01:04:38.400
come to their office hours or my office
01:04:34.480 --> 01:04:41.960
hours um I think they are visha are they
01:04:38.400 --> 01:04:43.880
posted on the site or okay yeah they
01:04:41.960 --> 01:04:47.240
they either are or will be posted on the
01:04:43.880 --> 01:04:49.720
site very soon um and come by to talk
01:04:47.240 --> 01:04:51.480
about anything uh if there's nobody in
01:04:49.720 --> 01:04:53.079
my office hours I'm happy to talk about
01:04:51.480 --> 01:04:54.599
things that are unrelated but if there's
01:04:53.079 --> 01:04:58.039
lots of people waiting outside or I
01:04:54.599 --> 01:05:00.319
might limit it to uh like um just things
01:04:58.039 --> 01:05:02.480
about the class so cool and we have
01:05:00.319 --> 01:05:04.760
Patza we'll be checking that regularly
01:05:02.480 --> 01:05:06.839
uh striving to get you an answer in 24
01:05:04.760 --> 01:05:12.240
hours on weekdays over weekends we might
01:05:06.839 --> 01:05:16.000
not so um yeah so that's all for today
01:05:12.240 --> 01:05:16.000
are there any questions
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