On the Capabilities of Pointer Networks for Deep Deductive Reasoning
  The importance of building neural networks that can learn to reason has been
well recognized in the neuro-symbolic community. In this paper, we apply neural
pointer networks for conducting reasoning over symbolic knowledge bases. In
doing so, we explore the benefits and limitations of encoder-decoder
architectures in general and pointer networks in particular for developing
accurate, generalizable and robust neuro-symbolic reasoners. Based on our
experimental results, pointer networks performs remarkably well across multiple
reasoning tasks while outperforming the previously reported state of the art by
a significant margin. We observe that the Pointer Networks preserve their
performance even when challenged with knowledge graphs of the domain/vocabulary
it has never encountered before. To the best of our knowledge, this is the
first study on neuro-symbolic reasoning using Pointer Networks. We hope our
impressive results on these reasoning problems will encourage broader
exploration of pointer networks' capabilities for reasoning over more complex
logics and for other neuro-symbolic problems.
