  In this paper, we report our methods and experiments for the TREC
Conversational Assistance Track (CAsT) 2022. In this work, we aim to reproduce
multi-stage retrieval pipelines and explore one of the potential benefits of
involving mixed-initiative interaction in conversational passage retrieval
scenarios: reformulating raw queries. Before the first ranking stage of a
multi-stage retrieval pipeline, we propose a mixed-initiative query
reformulation module, which achieves query reformulation based on the
mixed-initiative interaction between the users and the system, as the
replacement for the neural reformulation method. Specifically, we design an
algorithm to generate appropriate questions related to the ambiguities in raw
queries, and another algorithm to reformulate raw queries by parsing users'
feedback and incorporating it into the raw query. For the first ranking stage
of our multi-stage pipelines, we adopt a sparse ranking function: BM25, and a
dense retrieval method: TCT-ColBERT. For the second-ranking step, we adopt a
pointwise reranker: MonoT5, and a pairwise reranker: DuoT5. Experiments on both
TREC CAsT 2021 and TREC CAsT 2022 datasets show the effectiveness of our
mixed-initiative-based query reformulation method on improving retrieval
performance compared with two popular reformulators: a neural reformulator:
CANARD-T5 and a rule-based reformulator: historical query reformulator(HQE).
