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://www.aclweb.org/anthology/P19-1465
DOI:
10.18653/v1/P19-1465
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1465.pdf