Character-level Intra Attention Network for Natural Language Inference

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


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.
Anthology ID:
W17-5309
Volume:
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
RepEval | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–50
Language:
URL:
https://www.aclweb.org/anthology/W17-5309
DOI:
10.18653/v1/W17-5309
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5309.pdf