Simple and Effective Text Matching with Richer Alignment Features

Code

• github.com/hitvoice/RE2
  OfficialIn papertf★ 0
• github.com/alibaba-edu/simple-effective-text-matching-pytorch
  pytorch★ 0
• github.com/alibaba-edu/simple-effective-text-matching
  tf★ 0

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.

Tasks

Benchmark Results

Reproductions