Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference

Qianlong Du, Chengqing Zong, Keh-Yih Su


Abstract
This paper proposes to perform natural language inference with Word-Pair-Dependency-Triplets. Most previous DNN-based approaches either ignore syntactic dependency among words, or directly use tree-LSTM to generate sentence representation with irrelevant information. To overcome the problems mentioned above, we adopt Word-Pair-Dependency-Triplets to improve alignment and inference judgment. To be specific, instead of comparing each triplet from one passage with the merged information of another passage, we first propose to perform comparison directly between the triplets of the given passage-pair to make the judgement more interpretable. Experimental results show that the performance of our approach is better than most of the approaches that use tree structures, and is comparable to other state-of-the-art approaches.
Anthology ID:
C18-1035
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
414–425
Language:
URL:
https://aclanthology.org/C18-1035
DOI:
Bibkey:
Cite (ACL):
Qianlong Du, Chengqing Zong, and Keh-Yih Su. 2018. Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference. In Proceedings of the 27th International Conference on Computational Linguistics, pages 414–425, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference (Du et al., COLING 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/fix-dup-bibkey/C18-1035.pdf
Data
MultiNLISNLI