Character-level intra attention networks for natural language inference
Document typeConference lecture
Rights accessOpen Access
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.
CitationYang, H., Ruiz, M., Fonollosa, J. A. R. Character-level intra attention networks for natural language inference. A: Conference on Empirical Methods in Natural Language Processing. "Proceedings of the 2nd Workshop on Evaluating Vector-Space Representations for NLP". 2017, p. 46-50.