  Conversational search allows a user to interact with a search system in
multiple turns. A query is strongly dependent on the conversation context. An
effective way to improve retrieval effectiveness is to expand the current query
with historical queries. However, not all the previous queries are related to,
and useful for expanding the current query. In this paper, we propose a new
method to select relevant historical queries that are useful for the current
query. To cope with the lack of labeled training data, we use a pseudo-labeling
approach to annotate useful historical queries based on their impact on the
retrieval results. The pseudo-labeled data are used to train a selection model.
We further propose a multi-task learning framework to jointly train the
selector and the retriever during fine-tuning, allowing us to mitigate the
possible inconsistency between the pseudo labels and the changed retriever.
Extensive experiments on four conversational search datasets demonstrate the
effectiveness and broad applicability of our method compared with several
strong baselines.
