  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.
