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

作　　者：Xiaoming SHI Wanxiang CHE 

机构地区：[1]School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China

出　　处：《Frontiers of Computer Science》2023年第5期67-73,共7页中国计算机科学前沿（英文版）

摘　　要：Slot filling,to extract entities for specific types of information(slot),is a vitally important modular of dialogue systems for automatic diagnosis.Doctor responses can be regarded as the weak supervision of patient queries.In 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 annotation.However,weakly labeled data suffers from extremely noisy samples.To alleviate the problem,we propose a simple and effective Co-WeakTeaching method.The method trains two slot filling models simultaneously.These two models learn from two different weakly labeled data,ensuring learning from two aspects.Then,one model utilizes selected weakly labeled data generated by the other,iteratively.The 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 data.Experimental 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.

关 键 词：dialogue system slot filling CO-TEACHING 

分 类 号：O17[理学—数学]