Sentence Matching with Syntax- and Semantics-Aware BERT

Tao Liu, Xin Wang, Chengguo Lv, Ranran Zhen, Guohong Fu


Abstract
Sentence matching aims to identify the special relationship between two sentences, and plays a key role in many natural language processing tasks. However, previous studies mainly focused on exploiting either syntactic or semantic information for sentence matching, and no studies consider integrating both of them. In this study, we propose integrating syntax and semantics into BERT with sentence matching. In particular, we use an implicit syntax and semantics integration method that is less sensitive to the output structure information. Thus the implicit integration can alleviate the error propagation problem. The experimental results show that our approach has achieved state-of-the-art or competitive performance on several sentence matching datasets, demonstrating the benefits of implicitly integrating syntactic and semantic features in sentence matching.
Anthology ID:
2020.coling-main.293
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3302–3312
Language:
URL:
https://aclanthology.org/2020.coling-main.293
DOI:
10.18653/v1/2020.coling-main.293
Bibkey:
Cite (ACL):
Tao Liu, Xin Wang, Chengguo Lv, Ranran Zhen, and Guohong Fu. 2020. Sentence Matching with Syntax- and Semantics-Aware BERT. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3302–3312, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Sentence Matching with Syntax- and Semantics-Aware BERT (Liu et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.293.pdf
Data
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