Combating with extremely noisy samples in weakly supervised slot filling for automatic diagnosis

作     者：Xiaoming SHI Wanxiang CHE Xiaoming SHI;Wanxiang CHE

作者机构：School of Computer Science and TechnologyHarbin Institute of TechnologyHarbin 150001China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2023年第17卷第5期

页      面：67-73页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：The authors want to thank Sendong Zhao for discussion 

主　　题：dialogue system slot filling co-teaching 

摘      要：Slot filling,to extract entities for specific types of information(slot),is a vitally important modular of dialogue systems for automatic *** responses can be regarded as the weak supervision of patient *** this way,a large amount of weakly labeled data can be obtained from unlabeled diagnosis dialogue,alleviating the problem of costly and time-consuming data ***,weakly labeled data suffers from extremely noisy *** alleviate the problem,we propose a simple and effective Co-WeakTeaching *** method trains two slot filling models *** two models learn from two different weakly labeled data,ensuring learning from two ***,one model utilizes selected weakly labeled data generated by the other,*** model,obtained by the Co-WeakTeaching on weakly labeled data,can be directly tested on testing data or sequentially fine-tuned on a small amount of human-annotated *** results on these two settings illustrate the effectiveness of the method with an increase of 8.03%and 14.74%in micro and macro f1 scores,respectively.