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
Editors:
Samuel Bowman, Yoav Goldberg, Felix Hill, Angeliki Lazaridou, Omer Levy, Roi Reichart, Anders Søgaard
Venue:
RepEval
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–50
Language:
URL:
https://aclanthology.org/W17-5309
DOI:
10.18653/v1/W17-5309
Bibkey:
Cite (ACL):
Han Yang, Marta R. Costa-jussà, and José A. R. Fonollosa. 2017. Character-level Intra Attention Network for Natural Language Inference. In Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP, pages 46–50, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Character-level Intra Attention Network for Natural Language Inference (Yang et al., RepEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5309.pdf
Code
 yanghanxy/CIAN
Data
MultiNLISNLI