Enhancing alignment with context similarity for natural language inference

Abstract

Previous approaches generally use context information to improve the word representation but ignore the importance of context similarity in aligning tokens.Furthermore,most of them uniformly weight various local decisions during aggregation for the global judgment.However,local decisions related to various tokens can influence the final decision differently.In order to process these problems,an enhanced alignment mechanism was proposed,which jointly considers both token content and context similarity in computing the alignment weight for each token pair.Besides,a selection gate mechanism to weight local decisions differently was also proposed.Experimental results show that our performance is comparable to state-of-the-art approaches but better mimics human behavior,making it more interpretable.

Keywords

• textual entailment;ural language inference;word alignment