Abstract
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.
Abstract (translated)
对搜索和聊天助手的长期挑战是解决歧义查询的查询意图检测问题。在聊天搜索中提出明确的问题被广泛研究和视为解决查询歧义的有效方法。现有的工作已经探索了各种明确问题排名和生成的方法和途径。然而,由于缺少真实的聊天搜索数据,他们必须使用人工数据集进行训练,这限制了其在真实搜索场景的泛化能力。因此,行业表现出不愿在实际中实施这些方法的意愿,进一步停止了真实聊天搜索交互数据的可用性。上述困境可以表述为明确的查询生成和聊天搜索的启动问题。此外,即使我们拥有大规模的聊天日志,收集可以涵盖公开领域搜索场景所有可能的查询和主题的数据是不切实际的。在训练一个明确问题检索/生成模型时,适应偏差的风险仍然存在,这是另一个重要挑战。在本研究中,我们创新性地探讨在零样本环境下生成明确问题以克服启动问题,并提出了一种有限制的明确问题生成系统,该系统使用问题模板和查询特征来指导有效和精确的问题生成。实验结果显示,我们的方法比现有的零样本基准线表现优异得多。我们模型的输出也被人类标注表明,我们的方法生成了25.2%更自然的问题、18.1%更有用的问题、6.1%更少自然的问题和4%更少无用的问题。
URL
https://arxiv.org/abs/2301.12660
