A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effective solution to resolve query ambiguity. Existing work have explored various approaches for clarifying question ranking and generation. However, due to the lack of real conversational search data, they have to use artificial datasets for training, which limits their generalizability to real-world search scenarios. As a result, the industry has shown reluctance to implement them in reality, further suspending the availability of real conversational search interaction data. The above dilemma can be formulated as a cold start problem of clarifying question generation and conversational search in general. Furthermore, even if we do have large-scale conversational logs, it is not realistic to gather training data that can comprehensively cover all possible queries and topics in open-domain search scenarios. The risk of fitting bias when training a clarifying question retrieval/generation model on incomprehensive dataset is thus another important challenge. In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation. The experiment results show that our method outperforms existing state-of-the-art zero-shot baselines by a large margin. Human annotations to our model outputs also indicate our method generates 25.2\% more natural questions, 18.1\% more useful questions, 6.1\% less unnatural and 4\% less useless questions.

翻译：搜索与对话式助手的长期挑战在于歧义查询中的意图检测。在对话式搜索中提出澄清问题已被广泛研究并视为解决查询歧义的有效方案。现有工作探索了多种澄清问题排序与生成方法，但由于缺乏真实对话搜索数据，这些方法不得不依赖人工数据集进行训练，从而限制了其在真实搜索场景中的泛化能力。因此，行业对其实际部署持谨慎态度，进一步阻碍了真实对话搜索交互数据的获取。上述困境可系统化表述为澄清问题生成与对话式搜索的冷启动问题。此外，即便拥有大规模对话日志，在开放域搜索场景中收集能全面覆盖所有可能查询主题的训练数据仍不现实，基于不完整数据集训练澄清问题检索/生成模型时可能产生的拟合偏差成为另一重要挑战。本研究创新性地探索了零样本场景下的澄清问题生成方式以克服冷启动问题，并提出了约束型澄清问题生成系统，该系统通过问题模板与查询方面协同引导有效且精准的问题生成。实验结果表明，本方法在性能上显著超越现有最优零样本基线模型。对模型输出的人工标注显示，本方法生成的对话自然度提升25.2%，有用性提升18.1%，不自然表述减少6.1%，无效问题减少4%。