Simple And Effective Text Matching With Richer Alignment Features
Runqi Yang, Jianhai Zhang, Xing Gao, Feng Ji, Haiqing Chen . Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019 – 171 citations
[Paper]
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.