Can recursive neural tensor networks learn logical reasoning?

TL;DR

This paper evaluates whether recursive neural tensor networks can learn logical reasoning by training on a constructed corpus of logical inference examples, showing promising generalization capabilities.

Contribution

It introduces a new dataset of logical reasoning examples and demonstrates that recursive neural tensor networks can generalize to new reasoning patterns.

Findings

Models generalize well to new reasoning patterns

Recursive neural tensor networks capture aspects of logical inference

Performance drops in a few complex reasoning cases

Abstract

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.