Character-level Intra Attention Network for Natural Language Inference

Han Yang; Marta R. Costa-jussà; José A. R. Fonollosa

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arXiv · 1707.07469

Character-level Intra Attention Network for Natural Language Inference

Han Yang·Marta R. Costa-jussà·José A. R. Fonollosa

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Abstract

Natural language inference (NLI) is a central problem in language understanding. 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 capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.

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Han Yang, Marta R. Costa-jussà, José A. R. Fonollosa. 2017-07-24. Character-level Intra Attention Network for Natural Language Inference. https://arxiv.org/abs/1707.07469

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