Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning

TL;DR

This paper introduces interactive sentence representation models for Chinese medical question-answer matching, combining BERT with CNNs or BiGRU to improve semantic association understanding and outperform existing models.

Contribution

It proposes a novel Crossed BERT network combined with multi-scale CNNs or BiGRU for enhanced semantic feature learning in Chinese medical QA matching.

Findings

Model significantly outperforms state-of-the-art methods

Effective extraction of deep semantic and association information

Improved accuracy on cMedQA datasets

Abstract

Chinese medical question-answer matching is more challenging than the open-domain question answer matching in English. Even though the deep learning method has performed well in improving the performance of question answer matching, these methods only focus on the semantic information inside sentences, while ignoring the semantic association between questions and answers, thus resulting in performance deficits. In this paper, we design a series of interactive sentence representation learning models to tackle this problem. To better adapt to Chinese medical question-answer matching and take the advantages of different neural network structures, we propose the Crossed BERT network to extract the deep semantic information inside the sentence and the semantic association between question and answer, and then combine with the multi-scale CNNs network or BiGRU network to take the advantage of different structure of neural networks to learn more semantic features into the sentence representation. The experiments on the cMedQA V2.0 and cMedQA V1.0 dataset show that our model significantly outperforms all the existing state-of-the-art models of Chinese medical question answer matching.