Simple and Effective Text Matching with Richer Alignment Features

Runqi Yang, Jianhai Zhang, Xing Gao, Feng Ji, Haiqing Chen


Abstract
In this paper, we present a fast and strong neural approach for general purpose text matching applications. We explore what is sufficient to build a fast and well-performed text matching model and propose to keep three key features available for inter-sequence alignment: original point-wise features, previous aligned features, and contextual features while simplifying all the remaining components. We conduct experiments on four well-studied benchmark datasets across tasks of natural language inference, paraphrase identification and answer selection. The performance of our model is on par with the state-of-the-art on all datasets with much fewer parameters and the inference speed is at least 6 times faster compared with similarly performed ones.
Anthology ID:
P19-1465
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4699–4709
Language:
URL:
https://aclanthology.org/P19-1465
DOI:
10.18653/v1/P19-1465
Bibkey:
Cite (ACL):
Runqi Yang, Jianhai Zhang, Xing Gao, Feng Ji, and Haiqing Chen. 2019. Simple and Effective Text Matching with Richer Alignment Features. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4699–4709, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Simple and Effective Text Matching with Richer Alignment Features (Yang et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1465.pdf
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