Before revision:  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 .
After revision:  Along these lines , 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 ) between the manually-rewritten and automatically-generated queries to 4 from 22 points when compared with the best CAsT submission .