%0 Journal Article
%A LI Jinbin
%A LIU Jianyi
%A SUN Yawei
%A FU Jieqiong
%T Medical Question-Answer Matching Base on Adversarial Training
%D 2022
%R 10.13190/j.jbupt.2021-242
%J Journal of Beijing University of Posts and Telecommunications
%P 37-43
%V 45
%N 4
%X Compared with the question-answer matching task in the English open domain, the task in the Chinese professional medical field is more challenging.In view of the complexity of Chinese semantics and the diversity of medical data,most researchers focus on designing complex neural networks to explore deeper text semantics, and this kind of idea is relatively simple.At the same time, the neural network model is easy to make misjudgments due to small disturbances, and the poor generalization ability of the model.To solve these issues,a question-answer matching model is proposed based on adversarial training. A bidirectional pre-training encoder is used in this model to capture the semantic information of question-answer sentences and to obtain the corresponding vector representation. Then, adversarial samples are generated by adding a disturbance factor to the word embedding representation.Finally, the initial samples and adversarial samples are jointly input into the model with linear layers for classification prediction.Comparative experiments demonstrate that adversarial training can effectively improve the performance of the question-answer matching model on the cMedQA V2.0 dataset.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-242