Character-level intra attention networks for natural language inference

Abstract

Natural language inference (NLI) is a central problem in language understand- ing. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character- level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to cap- ture the intra-sentence semantics. The pro- posed CIAN model provides improved re- sults based on a newly published MNLI corpus.