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.

翻译：尽管构建和生成混合主动对话式搜索系统中澄清问题的研究已十分丰富，但针对用户对这类问题回答的处理与理解的研究仍较为匮乏。为此，我们提出了一种简单而有效的方法来处理澄清问题的回答，突破了以往仅将回答附加到原始查询中导致检索性能下降的局限。具体而言，我们设计了一个分类器，用于评估所生成的澄清问题及用户回答的效用。被判定为有效的问题或回答将被进一步追加到对话历史中，并输入基于Transformer的查询重写模块。实验结果表明，该方法相比于非混合主动的强基线模型取得了显著提升。此外，所提出的方法还能有效缓解使用无效问题及回答时引发的性能下降问题。