Users often have trouble formulating their information needs into words on the first try when searching online. This can lead to frustration, as they may have to reformulate their queries when retrieved information is not relevant. This can be due to a lack of familiarity with the specific terminology related to their search topic, or because queries are ambiguous and related to multiple topics. Most modern search engines have interactive features that suggest clarifications or similar queries based on what others have searched for. However, the proposed models are either based on a single interaction or evaluated on search logs, hindering the naturalness of the interactions. In this paper, we introduce CIRCLE, a generative model for multi-turn query Clarifications wIth ReinforCement LEarning that leverages multi-turn interactions through a user simulation framework. Our model aims at generating a diverse set of query clarifications using a pretrained language model fine-tuned using reinforcement learning. We evaluate it against well established google suggestions using a user simulation framework.

翻译：摘要：用户在在线搜索时，往往难以首次就将信息需求准确转化为文字表述。当检索到的信息与需求不相关时，用户可能需要重新组织查询语句，这容易导致挫败感。究其原因，可能是用户不熟悉与搜索主题相关的专业术语，或查询本身具有歧义且关联多个主题。现代搜索引擎大多具备交互式功能，能够基于其他用户的搜索行为提供澄清建议或相似查询。然而，现有模型要么仅支持单次交互，要么基于搜索日志进行评估，这限制了交互的自然性。本文提出CIRCLE——一种基于强化学习的多轮查询澄清生成模型，通过用户模拟框架实现多轮交互。该模型利用预训练语言模型，经强化学习微调后生成多样化的查询澄清方案。我们通过用户模拟框架将其与成熟的谷歌搜索建议进行了对比评估。