Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging...
With the availability
of large annotated data (Bowman et al., 2015), it has recently become feasible
to train neural network based inference models, which have shown to be very
effective. In this paper, we present a new state-of-the-art result, achieving
the accuracy of 88.6% on the Stanford Natural Language Inference Dataset. Unlike the previous top models that use very complicated network architectures,
we first demonstrate that carefully designing sequential inference models based
on chain LSTMs can outperform all previous models. Based on this, we further
show that by explicitly considering recursive architectures in both local
inference modeling and inference composition, we achieve additional
improvement. Particularly, incorporating syntactic parsing information
contributes to our best result---it further improves the performance even when
added to the already very strong model.