demo_data/nips-2021/25970/transcript_whisper_large-v2.vtt

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Hi, my name is Maxwell Nye, and today I'll be talking about improving coherence and consistency

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in neural sequence models with dual system neurosymbolic reasoning.

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So I first want to give a little bit of a demo, which is to ask this question.

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A bat and a ball cost $1.10 in total.

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The bat costs $1 more than the ball.

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How much does the ball cost?

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So I'll let you think a little bit for this.

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So one answer that sort of might jump out at you is $0.10, but this is actually incorrect

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because the sum of the two objects should be $1.10.

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So the correct answer is actually $0.05.

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And this is an example from a cognitive reflection test, and these are questions designed to

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have a particular answer which comes to mind quite quickly, which is in fact wrong.

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And something that's interesting is that large-scale language models such as GPT-3 predict the

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wrong answers as well.

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And this is true not just for the sort of the classic cognitive reflection test, but

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also for variants with different numbers.

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So this is sort of an interesting thing.

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It talks about how neural language models often have issues with consistency and coherence.

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So another place that we can see this a little more concretely is the clutter data set.

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In the clutter data set, models are trained to...

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There are sentences about people and their family relationships and stories about those

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people.

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And this was originally devised as a question-answering data set where you ask what the relations

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are.

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One thing you can do is ask models to be trained on this data set and then generate new stories.

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And when you do that, you'll see that often the generated stories have inconsistency.

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So if we look at the bottom of the screen here, we can see an example of this.

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Robert and his brother Antonio played harmonicas together.

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Robert's daughter, Elsie, asked him to play with her.

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Elsie doesn't like having to babysit her younger brother, Antonio.

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And so we can see that this is a common sense error because Elsie is not the younger brother

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of Antonio.

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Or Elsie's younger brother is not Antonio.

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So what we've done is we've built a dual system model using large-scale neural networks and

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symbolic deliberative logic in order to try to help with these consistency issues.

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So the model is as follows.

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You use neural generation to generate sentences in a particular story.

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You might generate the next sentence using a model such as GPT-3 or BART.

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What you can then do is parse that sentence into the semantic meaning with respect to

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the family relationships and check whether or not it matches the current state of the

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family relationships that's been described so far, and only accept the candidate sentence

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generations that are actually consistent.

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So this has a few components.

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One of the components here is a symbolic world model.

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In the case of this clutter domain, the symbolic world model that we built encodes people and

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their family relationships.

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So in other words, you could take a sentence and encode what the underlying family relationship

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is.

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And what you can do is you can use SMT solvers such as the Z3 solver to check consistency.

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So given a new sentence, you can check that it doesn't disobey the rules of ancestry that

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we've defined here.

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And so some of those are, for example, what is the relationship between children and grandchildren?

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And then another is what are the rules about whether ancestry, can you be your own ancestor,

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et cetera.

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So one question is how is this semantic parsing done?

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And it turns out we can actually do this quite cheaply using GPT-3.

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So what we can see here in the dotted box is an actual example of a few-shot prompt

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we can use to parse each new sentence, each new candidate sentence from the system one

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generation model and parse it into the semantic form that we can then give to the world model

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solver.

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So the results here show that models that use this dual system neurosymbolic stories

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show improved coherence over just sentences that were constructed by a neural model.

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So the example here is that what we've done is we've used human judgments on which of

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the following sentences make more sense given the prior context of the story.

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And we see that if we use a symbolic world model and the parsing scheme described above,

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humans prefer the judgments given by this model.

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We can also apply the same sort of reasoning to a completely different task.

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Here we can discuss the grounded instruction following task, the grounded instruction following

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domain called gscan.

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In this domain, the goal is to have an agent, which is shown by this pink triangle, follow

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a command to perform some simple action in this grid world.

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So you can see here, walk to a small yellow cylinder might be an example of a command.

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Prior work has shown that one thing you can do is encode the initial state, encode the

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instruction and then train a neural model to predict the action sequences.

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Other work has also shown that one thing you can do is train a model to predict a distribution

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over the correct target location as part of the neural model.

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That will also increase the performance of the model.

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What we do here is show that if you do both of these things, you predict both an action

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sequence and a target location, like what is the location you should end up in, and

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then check whether or not when you execute the set of instructions, you will end up in

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the predicted target location.

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You can sort of check consistency between these two different predictions and only accept

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those instruction sequences which match the target location prediction.

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And this leads to also higher accuracy, especially in a low data regime.

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We have more details about the results of the paper.

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So that's a little bit of an overview of our paper.

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Our takeaways are that you can build systems with combined neural methods and explicit

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world knowledge.

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And if you add just a little bit of world knowledge, you can really help increase coherence

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and consistency for these large sequence models.

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There are some challenges here about parsing in larger scale domains and also what it would

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mean to automatically build a more complete world model.

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Thank you very much.