Estimating the Usefulness of Clarifying Questions and Answers for
  Conversational Search
Ivan Sekulić Weronika Łajewska Krisztian Balog Fabio Crestani
While the body of research directed towards constructing and generating
clarifying questions in mixed-initiative conversational search systems is vast,
research aimed at processing and comprehending users' answers to such questions
is scarce. To this end, we present a simple yet effective method for processing
answers to clarifying questions, moving away from previous work that simply
appends answers to the original query and thus potentially degrades retrieval
performance. Specifically, we propose a classifier for assessing usefulness of
the prompted clarifying question and an answer given by the user. Useful
questions or answers are further appended to the conversation history and
passed to a transformer-based query rewriting module. Results demonstrate
significant improvements over strong non-mixed-initiative baselines.
Furthermore, the proposed approach mitigates the performance drops when non
useful questions and answers are utilized.