Answer Interaction in Non-factoid Question Answering Systems
Answer Interaction in Non-factoid Question Answering Systems
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DOI：
10.1145/3295750.3298946
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
Chen Qu;Liu Yang;W. Bruce Croft;Falk Scholer;Yongfeng Zhang
中科院分区：
文献类型：
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作者：
Chen Qu;Liu Yang;W. Bruce Croft;Falk Scholer;Yongfeng Zhang
信息检索系统正在从文献检索向答案检索发展。网络搜索日志提供了大量关于人们如何与排序的文档列表交互的数据，但对与答案文本的交互知之甚少。在本文中，我们使用Amazon Machine Turk来研究非事实问答环境下的三种答案呈现和交互方式。我们发现，人们对好答案和坏答案的感知和反应非常不同，并且可以相对较快地识别出好答案。我们的结果为进一步研究有效的答案交互和反馈方法提供了基础。
Information retrieval systems are evolving from document retrieval to answer retrieval. Web search logs provide large amounts of data about how people interact with ranked lists of documents, but very little is known about interaction with answer texts. In this paper, we use Amazon Mechanical Turk to investigate three answer presentation and interaction approaches in a non-factoid question answering setting. We find that people perceive and react to good and bad answers very differently, and can identify good answers relatively quickly. Our results provide the basis for further investigation of effective answer interaction and feedback methods.