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While the body of research directed towards constructing and generatingclarifying questions in mixed-initiative conversational search systems is vast,research aimed at processing and comprehending users' answers to such questionsis scarce. To this end, we present a simple yet effective method for processinganswers to clarifying questions, moving away from previous work that simplyappends answers to the original query and thus potentially degrades retrievalperformance. Specifically, we propose a classifier for assessing usefulness ofthe prompted clarifying question and an answer given by the user. Usefulquestions or answers are further appended to the conversation history andpassed to a transformer-based query rewriting module. Results demonstratesignificant improvements over strong non-mixed-initiative baselines.Furthermore, the proposed approach mitigates the performance drops when nonuseful questions and answers are utilized.

Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search