Simple and Effective Text Matching with Richer Alignment Features
Runqi Yang, Jianhai Zhang, Xing Gao, Feng Ji, Haiqing Chen · 2019
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.