I am very bored by language models that just pump the training data through something and then predict the word following a partially revealed sentence. I mean, I don’t learn anything from them apart from a lot of technical wizardry. So, I am trying to look for papers that lean towards true comprehension.
This paper “Question Asking as Program Generation” is from NIPS 2017. They have collected 605 questions that humans have asked in the context of several partially revealed battleship game board with a view to gain information about the positions of the hidden ships. So each of these questions comes with a context (the game board). The authors aim to create a system to predict the question given a context.
Questions as Programs
In order to generate questions we need something compositional. The authors turn to programming. In this case, lisp programs. Every question is written as a lisp program. For example,
How many tiles have the same colour as tile 2F? is written as
(setSize (colouredTiles (colour 2F))). These are converted by hand.
They have a limited set of allowed predicates and constructions as the domain is limited to this battleship board. As a result, they define a simple context-free grammar that covers all possible valid programs.
In the context of the battleship game, the human subjects were asked to come up with ‘useful’ questions. They were given plenty of training. It’s great having a grammar to generate valid programs but how many of them are actually useful? To that end, they define several features. I’ll describe these and skip over the probability model that is optimized.
The first is the Expected Information Gain value of a question. That is, how much uncertainty is reduced in knowing the actual board state after the answers (averaged over multiple contexts) to that question. So, questions which reveal more (on average) about the actual state get a higher score.
The second is complexity. Favoring the above feature only can lead to very long questions. So, this feature is thrown in to favor shorter questions. This measures the complexity of a question based on the grammar.
Another feature is the answer type. That is whether it is a yes/no question or a location or a colour.
What’s interesting is that they were able to generate questions (i.e. programs) given a context. It’s interesting to tackle a problem like this. It is similar to say a robot learning language with respect to its senses. Learning can be very quick because the language has to be computed unlike unsupervised language models.
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