Natural Language Inference over Interaction Space

Abstract

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.

15 pages, 2 figures, under review as ICLR proceeding, Published at Sixth International Conference on Learning Representations, ICLR 2018

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ADADELTA: An Adaptive Learning Rate Method
• Convolutional Neural Networks for Sentence Classification
• Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
• Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference

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