elplaguister/DTS_line_datasets · Datasets at Hugging Face

topic_shift bool 2 classes	utterance stringlengths 1 7.9k	session_id stringlengths 7 14
false	Oh , you don't know. OK.	QMSum_120
false	Yeah.	QMSum_120
false	Alright.	QMSum_120
false	Um , yeah. So the points were the the weights how to weight the different error rates that are obtained from different language and and conditions. Um , it 's not clear that they will keep the same kind of weighting. Right now it 's a weighting on on improvement.	QMSum_120
false	Mm - hmm.	QMSum_120
false	Some people are arguing that it would be better to have weights on uh well , to to combine error rates before computing improvement. Uh , and the fact is that for right now for the English , they have weights they they combine error rates , but for the other languages they combine improvement. So it 's not very consistent. Um	QMSum_120
false	Mm - hmm.	QMSum_120
false	Yeah. The , um Yeah. And so Well , this is a point. And right now actually there is a thing also , uh , that happens with the current weight is that a very non - significant improvement on the well - matched case result in huge differences in in the final number.	QMSum_120
false	Mm - hmm.	QMSum_120
false	And so , perhaps they will change the weights to	QMSum_120
false	Hmm.	QMSum_120
false	Yeah.	QMSum_120
false	How should that be done ? I mean , it it seems like there 's a simple way	QMSum_120
false	Mm - hmm.	QMSum_120
false	Uh , this seems like an obvious mistake or something.	QMSum_120
false	Well , I mean , the fact that it 's inconsistent is an obvious mistake.	QMSum_120
false	Th - they 're	QMSum_120
false	But the but , um , the other thing	QMSum_120
false	In	QMSum_120
false	I don't know I haven't thought it through , but one one would think that each It it 's like if you say what 's the what 's the best way to do an average , an arithmetic average or a geometric average ?	QMSum_120
false	Mm - hmm.	QMSum_120
false	It depends what you wanna show.	QMSum_120
false	Mm - hmm.	QMSum_120
false	Each each one is gonna have a different characteristic.	QMSum_120
false	Yeah.	QMSum_120
false	So	QMSum_120
false	Well , it seems like they should do , like , the percentage improvement or something , rather than the absolute improvement.	QMSum_120
false	Tha - that 's what they do.	QMSum_120
false	Well , they are doing that.	QMSum_120
false	Yeah.	QMSum_120
false	No , that is relative. But the question is , do you average the relative improvements or do you average the error rates and take the relative improvement maybe of that ?	QMSum_120
false	Yeah. Yeah.	QMSum_120
false	And the thing is it 's not just a pure average because there are these weightings.	QMSum_120
false	Oh.	QMSum_120
false	It 's a weighted average. Um.	QMSum_120
false	Yeah. And so when you average the the relative improvement it tends to to give a lot of of , um , importance to the well - matched case because the baseline is already very good and , um , i it 's	QMSum_120
false	Why don't they not look at improvements but just look at your av your scores ? You know , figure out how to combine the scores	QMSum_120
false	Mm - hmm.	QMSum_120
false	with a weight or whatever , and then give you a score here 's your score. And then they can do the same thing for the baseline system and here 's its score. And then you can look at	QMSum_120
false	Mm - hmm.	QMSum_120
false	Well , that 's what he 's seeing as one of the things they could do.	QMSum_120
false	Yeah.	QMSum_120
false	It 's just when you when you get all done , I think that they pro I m I I wasn't there but I think they started off this process with the notion that you should be significantly better than the previous standard.	QMSum_120
false	Mm - hmm.	QMSum_120
false	And , um , so they said " how much is significantly better ? what do you ? " And and so they said " well , you know , you should have half the errors , " or something , " that you had before ".	QMSum_120
false	Mm - hmm. Hmm.	QMSum_120
false	Mm - hmm.	QMSum_120
false	Yeah.	QMSum_120
false	So it 's , uh , But it does seem like	QMSum_120
false	Hmm.	QMSum_120
false	i i it does seem like it 's more logical to combine them first and then do the	QMSum_120
false	Combine error rates and then	QMSum_120
false	Yeah.	QMSum_120
false	Yeah. Well	QMSum_120
false	Yeah.	QMSum_120
false	But there is this this is this still this problem of weights. When when you combine error rate it tends to give more importance to the difficult cases , and some people think that	QMSum_120
false	Oh , yeah ?	QMSum_120
false	well , they have different , um , opinions about this. Some people think that it 's more important to look at to have ten percent imp relative improvement on well - matched case than to have fifty percent on the m mismatched , and other people think that it 's more important to improve a lot on the mismatch and So , bu	QMSum_120
false	It sounds like they don't really have a good idea about what the final application is gonna be.	QMSum_120
false	l de fff ! Mmm.	QMSum_120
false	Well , you know , the the thing is that if you look at the numbers on the on the more difficult cases , um , if you really believe that was gonna be the predominant use , none of this would be good enough.	QMSum_120
false	Yeah. Mmm. Yeah.	QMSum_120
false	Nothing anybody 's	QMSum_120
false	Mm - hmm.	QMSum_120
false	whereas you sort of with some reasonable error recovery could imagine in the better cases that these these systems working. So , um , I think the hope would be that it would uh , it would work well for the good cases and , uh , it would have reasonable reas soft degradation as you got to worse and worse conditions. Um.	QMSum_120
false	Yeah. I I guess what I 'm I mean , I I was thinking about it in terms of , if I were building the final product and I was gonna test to see which front - end I 'd I wanted to use , I would try to weight things depending on the exact environment that I was gonna be using the system in.	QMSum_120
false	But but No.	QMSum_120
false	If I	QMSum_120
false	Well , no well , no. I mean , it isn't the operating theater. I mean , they don they they don't they don't really know , I think.	QMSum_120
false	Yeah.	QMSum_120
false	I mean , I th	QMSum_120
false	So if if they don't know , doesn't that suggest the way for them to go ? Uh , you assume everything 's equal. I mean , y y I mean , you	QMSum_120
false	Well , I mean , I I think one thing to do is to just not rely on a single number to maybe have two or three numbers ,	QMSum_120
false	Yeah.	QMSum_120
false	you know ,	QMSum_120
false	Right.	QMSum_120
false	and and and say here 's how much you , uh you improve the , uh the the relatively clean case and here 's or or well - matched case , and here 's how here 's how much you ,	QMSum_120
false	Mm - hmm.	QMSum_120
false	uh	QMSum_120
false	So not	QMSum_120
false	So.	QMSum_120
false	So not try to combine them.	QMSum_120
false	Yeah. Uh , actually it 's true.	QMSum_120
false	Yeah.	QMSum_120
false	Uh , I had forgotten this , uh , but , uh , well - matched is not actually clean. What it is is just that , u uh , the training and testing are similar.	QMSum_120
false	The training and testing.	QMSum_120
false	Mmm.	QMSum_120
false	So , I guess what you would do in practice is you 'd try to get as many , uh , examples of similar sort of stuff as you could , and then ,	QMSum_120
false	Yeah.	QMSum_120
false	uh So the argument for that being the the the more important thing , is that you 're gonna try and do that , but you wanna see how badly it deviates from that when when when the , uh it 's a little different.	QMSum_120
false	So	QMSum_120
false	Um ,	QMSum_120
false	so you should weight those other conditions v very you know , really small.	QMSum_120
false	But No. That 's a that 's a that 's an arg	QMSum_120
false	I mean , that 's more of an information kind of thing.	QMSum_120
false	that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.	QMSum_120
false	Uh - huh.	QMSum_120
false	I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?	QMSum_120
false	Mm - hmm.	QMSum_120
false	With what passing you ? With uh , I mean ,	QMSum_120

false

Oh , you don't know. OK.

QMSum_120

false

Yeah.

QMSum_120

false

Alright.

QMSum_120

false

Um , yeah. So the points were the the weights how to weight the different error rates that are obtained from different language and and conditions. Um , it 's not clear that they will keep the same kind of weighting. Right now it 's a weighting on on improvement.

QMSum_120

false

Mm - hmm.

QMSum_120

false

Some people are arguing that it would be better to have weights on uh well , to to combine error rates before computing improvement. Uh , and the fact is that for right now for the English , they have weights they they combine error rates , but for the other languages they combine improvement. So it 's not very consistent. Um

QMSum_120

false

Mm - hmm.

QMSum_120

false

Yeah. The , um Yeah. And so Well , this is a point. And right now actually there is a thing also , uh , that happens with the current weight is that a very non - significant improvement on the well - matched case result in huge differences in in the final number.

QMSum_120

false

Mm - hmm.

QMSum_120

false

And so , perhaps they will change the weights to

QMSum_120

false

Hmm.

QMSum_120

false

Yeah.

QMSum_120

false

How should that be done ? I mean , it it seems like there 's a simple way

QMSum_120

false

Mm - hmm.

QMSum_120

false

Uh , this seems like an obvious mistake or something.

QMSum_120

false

Well , I mean , the fact that it 's inconsistent is an obvious mistake.

QMSum_120

false

Th - they 're

QMSum_120

false

But the but , um , the other thing

QMSum_120

false

In

QMSum_120

false

I don't know I haven't thought it through , but one one would think that each It it 's like if you say what 's the what 's the best way to do an average , an arithmetic average or a geometric average ?

QMSum_120

false

Mm - hmm.

QMSum_120

false

It depends what you wanna show.

QMSum_120

false

Mm - hmm.

QMSum_120

false

Each each one is gonna have a different characteristic.

QMSum_120

false

Yeah.

QMSum_120

false

So

QMSum_120

false

Well , it seems like they should do , like , the percentage improvement or something , rather than the absolute improvement.

QMSum_120

false

Tha - that 's what they do.

QMSum_120

false

Well , they are doing that.

QMSum_120

false

Yeah.

QMSum_120

false

No , that is relative. But the question is , do you average the relative improvements or do you average the error rates and take the relative improvement maybe of that ?

QMSum_120

false

Yeah. Yeah.

QMSum_120

false

And the thing is it 's not just a pure average because there are these weightings.

QMSum_120

false

Oh.

QMSum_120

false

It 's a weighted average. Um.

QMSum_120

false

Yeah. And so when you average the the relative improvement it tends to to give a lot of of , um , importance to the well - matched case because the baseline is already very good and , um , i it 's

QMSum_120

false

Why don't they not look at improvements but just look at your av your scores ? You know , figure out how to combine the scores

QMSum_120

false

Mm - hmm.

QMSum_120

false

with a weight or whatever , and then give you a score here 's your score. And then they can do the same thing for the baseline system and here 's its score. And then you can look at

QMSum_120

false

Mm - hmm.

QMSum_120

false

Well , that 's what he 's seeing as one of the things they could do.

QMSum_120

false

Yeah.

QMSum_120

false

It 's just when you when you get all done , I think that they pro I m I I wasn't there but I think they started off this process with the notion that you should be significantly better than the previous standard.

QMSum_120

false

Mm - hmm.

QMSum_120

false

And , um , so they said " how much is significantly better ? what do you ? " And and so they said " well , you know , you should have half the errors , " or something , " that you had before ".

QMSum_120

false

Mm - hmm. Hmm.

QMSum_120

false

Mm - hmm.

QMSum_120

false

Yeah.

QMSum_120

false

So it 's , uh , But it does seem like

QMSum_120

false

Hmm.

QMSum_120

false

i i it does seem like it 's more logical to combine them first and then do the

QMSum_120

false

Combine error rates and then

QMSum_120

false

Yeah.

QMSum_120

false

Yeah. Well

QMSum_120

false

Yeah.

QMSum_120

false

But there is this this is this still this problem of weights. When when you combine error rate it tends to give more importance to the difficult cases , and some people think that

QMSum_120

false

Oh , yeah ?

QMSum_120

false

well , they have different , um , opinions about this. Some people think that it 's more important to look at to have ten percent imp relative improvement on well - matched case than to have fifty percent on the m mismatched , and other people think that it 's more important to improve a lot on the mismatch and So , bu

QMSum_120

false

It sounds like they don't really have a good idea about what the final application is gonna be.

QMSum_120

false

l de fff ! Mmm.

QMSum_120

false

Well , you know , the the thing is that if you look at the numbers on the on the more difficult cases , um , if you really believe that was gonna be the predominant use , none of this would be good enough.

QMSum_120

false

Yeah. Mmm. Yeah.

QMSum_120

false

Nothing anybody 's

QMSum_120

false

Mm - hmm.

QMSum_120

false

whereas you sort of with some reasonable error recovery could imagine in the better cases that these these systems working. So , um , I think the hope would be that it would uh , it would work well for the good cases and , uh , it would have reasonable reas soft degradation as you got to worse and worse conditions. Um.

QMSum_120

false

Yeah. I I guess what I 'm I mean , I I was thinking about it in terms of , if I were building the final product and I was gonna test to see which front - end I 'd I wanted to use , I would try to weight things depending on the exact environment that I was gonna be using the system in.

QMSum_120

false

But but No.

QMSum_120

false

If I

QMSum_120

false

Well , no well , no. I mean , it isn't the operating theater. I mean , they don they they don't they don't really know , I think.

QMSum_120

false

Yeah.

QMSum_120

false

I mean , I th

QMSum_120

false

So if if they don't know , doesn't that suggest the way for them to go ? Uh , you assume everything 's equal. I mean , y y I mean , you

QMSum_120

false

Well , I mean , I I think one thing to do is to just not rely on a single number to maybe have two or three numbers ,

QMSum_120

false

Yeah.

QMSum_120

false

you know ,

QMSum_120

false

Right.

QMSum_120

false

and and and say here 's how much you , uh you improve the , uh the the relatively clean case and here 's or or well - matched case , and here 's how here 's how much you ,

QMSum_120

false

Mm - hmm.

QMSum_120

false

uh

QMSum_120

false

So not

QMSum_120

false

So.

QMSum_120

false

So not try to combine them.

QMSum_120

false

Yeah. Uh , actually it 's true.

QMSum_120

false

Yeah.

QMSum_120

false

Uh , I had forgotten this , uh , but , uh , well - matched is not actually clean. What it is is just that , u uh , the training and testing are similar.

QMSum_120

false

The training and testing.

QMSum_120

false

Mmm.

QMSum_120

false

So , I guess what you would do in practice is you 'd try to get as many , uh , examples of similar sort of stuff as you could , and then ,

QMSum_120

false

Yeah.

QMSum_120

false

uh So the argument for that being the the the more important thing , is that you 're gonna try and do that , but you wanna see how badly it deviates from that when when when the , uh it 's a little different.

QMSum_120

false

So

QMSum_120

false

Um ,

QMSum_120

false

so you should weight those other conditions v very you know , really small.

QMSum_120

false

But No. That 's a that 's a that 's an arg

QMSum_120

false

I mean , that 's more of an information kind of thing.

QMSum_120

false

that 's an ar Well , that 's an argument for it , but let me give you the opposite argument. The opposite argument is you 're never really gonna have a good sample of all these different things.

QMSum_120

false

Uh - huh.

QMSum_120

false

I mean , are you gonna have w uh , uh , examples with the windows open , half open , full open ? Going seventy , sixty , fifty , forty miles an hour ? On what kind of roads ?

QMSum_120

false

Mm - hmm.

QMSum_120

false

With what passing you ? With uh , I mean ,

QMSum_120