Character-level Intra Attention Network for Natural Language Inference
Han Yang | Marta R. Costa-jussà | José A. R. Fonollosa
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
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.