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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-5309.pdf
- Code
- yanghanxy/CIAN
- Data
- MultiNLI, SNLI