摘要
为解决问答系统中问答句之间语义信息交互较少的问题,增强模型对问题分类信息的应用,提出一种将问题分类和预训练模型BERT相结合的答案选择模型。通过问题分类获取问句的期望答案类型,根据问句的期望答案类型遮蔽候选答案句中无关的单词,利用BERT模型更深层次的融合问题句和答案句中句法和语义特征,计算问答对的语义相似度。实验结果表明,采用融合问题分类信息的答案选择模型,在TrecQA Clean和WikiQA数据集上的MAP和MRR指标都有明显提升。
To solve the problem of less interaction of semantic information between question and answer sentences in question answering system and to enhance the application of question classification information in the model,an answer selection model combining question classification and pre-training model BERT was proposed.The expected answer type of the question sentence was obtained through the question classification,and the irrelevant words in the candidate answer sentence were masked accor-ding to the expected answer type of the question sentence,the syntactic and semantic features of question sentence and answer sentence were deeply fused using BERT model to calculate the semantic similarity of question answer pairs.Experimental results show that the answer selection model integrating question classification information significantly improves the MAP and MRR indicators on TrecQA Clean and WikiQA data sets.
作者
何丽
张家铭
徐丽闪
王昊
李欣
HE Li;ZHANG Jia-ming;XU Li-shan;WANG Hao;LI Xin(School of Information,North China University of Technology,Beijing 100144,China;CNONIX National Standard Application and Promotion Laboratory,North China University of Technology,Beijing 100144,China)
出处
《计算机工程与设计》
北大核心
2023年第5期1412-1418,共7页
Computer Engineering and Design
基金
国家自然科学基金项目(61972003、61672040)。
关键词
问答系统
问题分类
深度模型
答案选择
期望答案类型
语义交互
BERT模型
question answering system
question classification
deep model
answer selection
expected answer type
semantic interaction
BERT model
