@inproceedings{liu-etal-2020-sentence,
title = "Sentence Matching with Syntax- and Semantics-Aware {BERT}",
author = "Liu, Tao and
Wang, Xin and
Lv, Chengguo and
Zhen, Ranran and
Fu, Guohong",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.293",
doi = "10.18653/v1/2020.coling-main.293",
pages = "3302--3312",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Sentence Matching with Syntax- and Semantics-Aware BERT
%A Liu, Tao
%A Wang, Xin
%A Lv, Chengguo
%A Zhen, Ranran
%A Fu, Guohong
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F liu-etal-2020-sentence
%X 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.
%R 10.18653/v1/2020.coling-main.293
%U https://aclanthology.org/2020.coling-main.293
%U https://doi.org/10.18653/v1/2020.coling-main.293
%P 3302-3312
Markdown (Informal)
[Sentence Matching with Syntax- and Semantics-Aware BERT](https://aclanthology.org/2020.coling-main.293) (Liu et al., COLING 2020)
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.