Medical Question Retrieval Based on Siamese Neural Network and Transfer Learning Method

Abstract

The online medical community websites have attracted an increase number of users in China. Patients post their questions on these sites and wait for professional answers from registered doctors. Most of these websites provide medical QA information related to the newly posted question by retrieval system. Previous researches regard such problem as question matching task: given a pair of questions, the supervised models learn question representation and predict it similar or not. In addition, there does not exist a finely annotated question pairs dataset in Chinese medical domain. In this paper, we declare two generation approaches to build large similar question datasets in Chinese health care domain. We propose a novel deep learning based architecture Siamese Text Matching Transformer model (STMT) to predict the similarity of two medical questions. It utilizes modified Transformer as encoder to learn question representation and interaction without extra manual lexical and syntactic resource. We design a data-driven transfer strategy to pre-train encoders and fine-tune models on different datasets. The experimental results show that the proposed model is capable of question matching task on both classification and ranking metrics.