Assistant Professor at the Center for Intelligent Information Retrieval of the University of Massachusetts Amherst
While conversational search has roots in early information retrieval research, recent advances in automatic speech recognition and conversational agents as well as popularity of devices with limited bandwidth interfaces have led to increasing interest in this area. An ideal conversational search system requires to go beyond the typical “query-response” paradigm by supporting mixed-initiative interactions. In this talk, I will review the recent efforts on developing mixed-initiative conversational search systems and draw connections with early work on interactive information retrieval. I will describe methods for generating and evaluating clarifying questions in response to search queries. I will further highlight the connections between conversational search and recommendation, and finish with a discussion on the next steps that require significant progress in the context of mixed-initiative conversational search.
My talk was recorded and I will join you for a live Q&A session after the recorded tutorial is played.
Hamed Zamani is an Assistant Professor at the Center for Intelligent Information Retrieval of the University of Massachusetts Amherst. His research focuses on designing and evaluating statistical and machine learning models with applications to (interactive) information access systems, including search engines, recommender systems, and question answering. He is mostly known for his work on neural information retrieval and weak supervision for information retrieval. Prior to UMass, he was a Researcher at Microsoft AI Research. Hamed received his Ph.D. in Computer Science from UMass under direction of W. Bruce Croft. During his Ph.D., he completed three summer internships at Microsoft AI Research (2017, 2019) and Google Research (2016). He obtained his M.Sc. and B.Sc. degrees from the University of Tehran.