Passage retrieval in a conversational context is essential for many downstream applications; it is however extremely challenging due to limited data resources. To address this problem, we present an effective multi-stage pipeline for passage ranking in conversational searchthat integrates a widely-used IR system with a conversational query reformulation module. Along these lines , we propose two simple yet effective query reformulation approaches: historical query expansion (HQE) and neural transfer reformulation (NTR) . Whereas HQE applies query expansion, a traditional IR query reformulation technique, NTR transfers human knowledge of conversational query understanding to a neural query reformulation model. The proposed HQE method was the top-performing submission of automatic systems in CAsT Track at TREC 2019. Building on this, our NTR approach improves an additional 18\% over that best entry in terms of NDCG@3. We further analyze the distinct behaviors of the two approaches, and show that fusing their output reduces the performance gap (measured in NDCG@3 ) between the manually-rewritten and automatically-generated queries to 4 from 22 points when compared with the best CAsT submission .
Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad-hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this paper, we tackle conversational passage retrieval (ConvPR), an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a multi-stage ad-hoc IR system. Specifically , we propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model. Detailed analyses of the two CQR methods are provided quantitatively and qualitatively, explaining their advantages, disadvantages, and distinct behaviors. Moreover, to leverage the strengths of both CQR methods, we propose combining their output with reciprocal rank fusion, yielding state-of-the-art retrieval effectiveness, 30\% improvement in terms of NDCG@3 compared to the best submission of TREC CAsT 2019.
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Passage retrieval in a conversational context is essential for many downstream applications; it is however extremely challenging due to limited data resources. To address this problem, we present an effective
Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad-hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this paper, we tackle conversational passage retrieval (ConvPR), an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a
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pipeline for passage ranking in conversational searchthat integrates a widely-used IR system with a conversational query reformulation module. Along these lines
ad-hoc IR system. Specifically
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simple yet effective query reformulation approaches: historical query expansion (HQE) and neural transfer reformulation (NTR) . Whereas HQE applies query expansion, a traditional IR query reformulation technique, NTR transfers human knowledge of conversational query understanding to a neural query reformulation model. The proposed HQE method was the top-performing submission of automatic systems in CAsT Track at TREC 2019. Building on this, our NTR approach improves an additional 18\% over that best entry in terms of NDCG@3. We further analyze the distinct behaviors of the two approaches, and show that fusing their output reduces the performance gap (measured in
conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model. Detailed analyses of the two CQR methods are provided quantitatively and qualitatively, explaining their advantages, disadvantages, and distinct behaviors. Moreover, to leverage the strengths of both CQR methods, we propose combining their output with reciprocal rank fusion, yielding state-of-the-art retrieval effectiveness, 30\% improvement in terms of
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) between the manually-rewritten and automatically-generated queries to 4 from 22 points when compared with the best CAsT submission .
compared to the best submission of TREC CAsT 2019.