Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse

Sheng Xu, Peifeng Li, Guodong Zhou, Qiaoming Zhu

Abstract
The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information. In this paper, we propose a novel text matching network (TMN) that encodes the discourse units and the paragraphs by combining Bi-LSTM and CNN to capture both global dependency information and local n-gram information. Moreover, it introduces three components of text matching, the Cosine, Bilinear and Single Layer Network, to incorporate various similarities and interactions among the discourse units. Experimental results on the Chinese Discourse TreeBank show that our proposed TMN model significantly outperforms various strong baselines in both micro-F1 and macro-F1.
Anthology ID:
C18-1044
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:
525–535
Language:
URL:
https://aclanthology.org/C18-1044
DOI:
Bibkey:
Cite (ACL):
Sheng Xu, Peifeng Li, Guodong Zhou, and Qiaoming Zhu. 2018. Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse. In Proceedings of the 27th International Conference on Computational Linguistics, pages 525–535, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse (Xu et al., COLING 2018)
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https://preview.aclanthology.org/fix-dup-bibkey/C18-1044.pdf
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