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The rise of intelligent assistant systems like Siri and Alexa have led to theemergence of Conversational Search, a research track of Information Retrieval(IR) that involves interactive and iterative information-seeking user-systemdialog. Recently released OR-QuAC and TCAsT19 datasets narrow their researchfocus on the retrieval aspect of conversational search i.e. fetching therelevant documents (passages) from a large collection using the conversationalsearch history. Currently proposed models for these datasets incorporatehistory in retrieval by appending the last N turns to the current questionbefore encoding. We propose to use another history selection approach thatdynamically selects and weighs history turns using the attention mechanism forquestion embedding. The novelty of our approach lies in experimenting with softattention-based history selection approach in an open-retrieval setting.

Role of Attentive History Selection in Conversational Information Seeking