  Natural Language Inference (NLI) task requires an agent to determine the
logical relationship between a natural language premise and a natural language
hypothesis. We introduce Interactive Inference Network (IIN), a novel class of
neural network architectures that is able to achieve high-level understanding
of the sentence pair by hierarchically extracting semantic features from
interaction space. We show that an interaction tensor (attention weight)
contains semantic information to solve natural language inference, and a denser
interaction tensor contains richer semantic information. One instance of such
architecture, Densely Interactive Inference Network (DIIN), demonstrates the
state-of-the-art performance on large scale NLI copora and large-scale NLI
alike corpus. It's noteworthy that DIIN achieve a greater than 20% error
reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to
the strongest published system.
