湖南工业大学学报2024，Vol.38Issue(3)：38-44,7.DOI:10.3969/j.issn.1673-9833.2024.03.006
基于多任务学习的知识库问答方法
A Knowledge Base Question-Answer Method Based on Multi-Task Learning
摘要
Abstract
In view of such flaws as error transmission or loose connection between different subtasks found in the traditional assembly line method of knowledge base Q&A,a new method of knowledge base Q&A system has been proposed,with multi-task learning incorporated into the knowledge base quiz system so as to improve its effectiveness.Allowing multiple subtasks to share a single encoder enables the model to acquire a better underlying representation,thus helping to improve the generalization ability of the model.Experimental results on the CCKS2022-CKBQA task verifies the better performance of the proposed method in this paper.关键词
知识库问答/自然语言处理/BERT/多任务学习Key words
knowledge base Q&A/natural language processing(NLP)/BERT/multi-task learning分类
信息技术与安全科学引用本文复制引用
金书川,朱艳辉,沈加锐,满芳藤,张志轩..基于多任务学习的知识库问答方法[J].湖南工业大学学报,2024,38(3):38-44,7.基金项目
国家自然科学基金资助项目(62106074) （62106074）
湖南省教育厅科研基金资助重点项目(22A0408,21A0350) （22A0408,21A0350）
湖南省自然科学基金资助项目(2022JJ50051) （2022JJ50051）
