Can recursive neural tensor networks learn logical reasoning?

Can recursive neural tensor networks learn logical reasoning? by Samuel R. Bowman.


Recursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoning. To evaluate this, I train a recursive model on a new corpus of constructed examples of logical reasoning in short sentences, like the inference of “some animal walks” from “some dog walks” or “some cat walks,” given that dogs and cats are animals. This model learns representations that generalize well to new types of reasoning pattern in all but a few cases, a result which is promising for the ability of learned representation models to capture logical reasoning.

From the introduction:

Natural language inference (NLI), the ability to reason about the truth of a statement on the basis of some premise, is among the clearest examples of a task that requires comprehensive and accurate natural language understanding [6].

I stumbled over that line in Samuel’s introduction because it implies, at least to me, that there is a notion of truth that resides outside of ourselves as speakers and hearers.

Take his first example:

Consider the statement all dogs bark. From this, one can infer quite a number of other things. One can replace the first argument of all (the first of the two predicates following it, here dogs) with any more specific category that contains only dogs and get a valid inference: all puppies bark; all collies bark.

Contrast that with one the premises that starts my day:

All governmental statements are lies of omission or commission.

Yet, firmly holding that as a “fact” of the world, I write to government officials, post ranty blog posts about government policies, urge others to attempt to persuade government to take certain positions.

Or as Leonard Cohen would say:

Everybody knows that the dice are loaded

Everybody rolls with their fingers crossed

It’s not that I think Samuel is incorrect about monotonicity for “logical reasoning” but monotonicity is a far cry from how people reason day to day.

Rather than creating “reasoning” that is such a departure from human inference, why not train a deep learning system to “reason” by exposing it to the same inputs and decisions made by human decision makers? Imitation doesn’t require understanding of human “reasoning,” just the ability to engage in the same behavior under similar circumstances.

That would reframe Samuel’s question to read: Can recursive neural tensor networks learn human reasoning?

I first saw this in a tweet by Sharon L. Bolding.

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