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In this work we focus on multi-turn passage retrieval as a crucial componentof conversational search. One of the key challenges in multi-turn passageretrieval comes from the fact that the current turn query is oftenunderspecified due to zero anaphora, topic change, or topic return. Contextfrom the conversational history can be used to arrive at a better expression ofthe current turn query, defined as the task of query resolution. In this paper,we model the query resolution task as a binary term classification problem: foreach term appearing in the previous turns of the conversation decide whether toadd it to the current turn query or not. We propose QuReTeC (Query Resolutionby Term Classification), a neural query resolution model based on bidirectionaltransformers. We propose a distant supervision method to automatically generatetraining data by using query-passage relevance labels. Such labels are oftenreadily available in a collection either as human annotations or inferred fromuser interactions. We show that QuReTeC outperforms state-of-the-art models,and furthermore, that our distant supervision method can be used tosubstantially reduce the amount of human-curated data required to trainQuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrievalarchitecture and demonstrate its effectiveness on the TREC CAsT dataset.

Query Resolution for Conversational Search with Limited Supervision