Can recursive neural tensor networks learn logical reasoning?
This addresses the ability of learned representation models to handle logical reasoning, which is incremental as it builds on existing recursive neural network approaches.
The paper tackled the problem of whether recursive neural tensor networks can capture logical reasoning by training a model on a constructed corpus of logical inferences, and found that it generalized well to new reasoning patterns in most cases.
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.