Asking Clarifying Questions Based on Negative Feedback in Conversational Search
Asking Clarifying Questions Based on Negative Feedback in Conversational Search
复制标题
DOI：
10.1145/3471158.3472232
复制
发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Keping Bi;Qingyao Ai;W. Bruce Croft
中科院分区：
文献类型：
--
作者：
Keping Bi;Qingyao Ai;W. Bruce Croft
当用户有复杂的信息搜索需求时，他们通常需要浏览多个搜索结果页面或重新制定查询。会话式搜索系统可以通过提问来澄清用户的搜索意图，从而提高用户满意度。然而，要回答一系列以“what/why/how”开头的问题，可能需要付出很大的努力。为了快速识别用户意图并减少交互过程中的工作量，我们提出了一个基于是/否问题的意图澄清任务，系统需要在最少的会话回合内询问有关意图的正确问题。在这个任务中，使用关于对话历史中先前问题的负面反馈是很重要的。为此，我们提出了一个基于最大边际相关性（MMR）的BERT模型（MMR-BERT），以利用基于MMR原则的负反馈来进行下一个澄清问题的选择。在Qulac数据集上的实验表明，MMR-BERT在意图识别任务上的表现明显优于最先进的基线，所选问题在相关文档检索任务上的表现也明显更好。
Users often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possible to improve user satisfaction by asking questions to clarify users' search intents. This, however, can take significant effort to answer a series of questions starting with "what/why/how". To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns. In this task, it is essential to use negative feedback about the previous questions in the conversation history. To this end, we propose a Maximum-Marginal-Relevance (MMR) based BERT model (MMR-BERT) to leverage negative feedback based on the MMR principle for the next clarifying question selection. Experiments on the Qulac dataset show that MMR-BERT outperforms state-of-the-art baselines significantly on the intent identification task and the selected questions also achieve significantly better performance in the associated document retrieval tasks.