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.

我们提出了一个简单而有效的方法来处理对澄清问题的回答，并介绍了一个对用户提出的问题和回答进行有用性评估的分类器，将有用的问题或回答附加到对话历史中，并传递给基于 transformer 的查询重写模块，实验证明与非混合主动的基线相比有显著改进。此外，所提出的方法缓解了使用不实用的问题和回答时性能下降的问题。