报告题目：Leveraging Conversation Context for Conversational Search

报告人：Jian-Yun Nie

报告时间：2023年8月31日下午14:30

报告地点：西华大学第6教学楼A区519室

主办单位：计算机与软件工程学院

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报告人简介：

Jian-Yun Nie is a professor at the Department of Computer Science and Operations Research, University of Montreal, and Canada research chair on natural language processing and applications. His research focuses on various problems of information retrieval and natural language processing, including information retrieval models, web search, cross-language information retrieval, recommendation systems, query suggestion, question answering and dialogue. Jian-Yun Nie has published over 250 papers in the main journals and conferences in IR and NLP. He is an associate editor of 4 journals. He has served as general chair, PC chair and local organization chair for SIGIR conferences, as well as for several other conferences and workshops. He regularly serves as senior PC members of major conferences such as SIGIR, CIKM, ACL, EMNLP, COLING, WWW. He received several best paper awards, including a Best paper award and a Test-of-Time honorable mention award from SIGIR. He is inducted to the ACM SIGIR Academy in 2022 for his contributions to the IR field.

 主要内容：

Search interface is evolving from a single short query to more natural and interactive ones such as conversational interface. The most distinctive characteristic in conversational search is the dependency of the search intent on the past conversation history. The query should often be reformulated to incorporate the conversation context information. The existing literature has found that conversational search can be improved by query rewriting based on a generative language model, or by simply concatenating all the historical queries. In this talk, we will see that the conversational context is very noisy: some conversation turns are unrelated to the current query, thus should be discarded. We propose a selection process to incorporate only the related historical queries based on their potential usefulness. To this end, an automatic labeling approach is used to label the historical queries according to their impact on the retrieval effectiveness. A selection model is then trained. The experiments show that such a selection can improve the effectiveness of conversational search. This work also demonstrates the necessity of developing specific approaches for conversational IR.