Can recursive neural tensor networks learn logical reasoning?
Authors: Samuel R. Bowman
ICLR 2014

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. The results are promising for the ability of these models to capture logical reasoning, but the model tested here appears to learn representations that are quite specific to the templatic structures of the problems seen in training, and that generalize beyond them only to a limited degree.


Peer review:
[Review]:
This paper investigates the use of a recurrent model for logical
reasoning in short sentences. An important part of the paper is
dedicated to the description on the task and the way the author
simplifies the task of MacCartney to keep only entailment relations
that are non ambiguous. For the model, a simple recurrent tensor (from
Socher's work) network is used.

While the more general task defined by MacCartney is well described,
the reduced task addressed in this paper is more unclear. The
motivation stands: this is a great idea to reduce the task to non
ambiguous cases, for which we could better interpret the experimental
results. However, at the end, it is difficult to draw relevant
conclusion from the experiments, and a lot of technical details are
missing to yield the results reproducible. Maybe the author tried to
lessen the negative aspects of the results, but it would be really
more interesting to clearly describe negative results. My opinion is
that this paper is not well suited for the conference track, and maybe
it should be submitted to the workshop track.