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:长期以来,搜索和谈话助理面临的一项挑战是在模糊的查询中发现意图; 询问对话搜索中的澄清问题的问题,已经得到广泛研究,并被认为是解决问题模糊性的有效解决办法; 现有工作探索了澄清问题排名和生成的各种方法; 然而,由于缺乏真正的对话搜索数据,他们不得不使用人工数据集进行培训,这限制了它们对于现实世界搜索情景的可概括性。因此,该行业表现出不愿意在现实中执行这些数据,进一步中止提供真正的对话搜索互动数据。上述两难困境可以被发展成一个澄清问题生成和一般对话搜索的寒冷开端问题。此外,即使我们确实有大规模对话日志,收集各种澄清问题排名和生成的方法也是不切实际的。 2 收集培训数据以全面涵盖公开搜索情景中所有可能的查询和专题是不现实的。 因此,在培训一个澄清问题检索/生成模型时可能存在偏差的风险是另一个重要的挑战。 在这项工作中,我们创新地探讨在零镜头中提出澄清问题,以克服冷淡的生成问题和一般对话搜索时间段。 我们提议一个有限的解问题生成系统, 以更精确的25号的基线 显示我们的现有方法 的生成结果。