As the popularity of voice assistants continues to surge, conversational search has gained increased attention in Information Retrieval. However, data sparsity issues in conversational search significantly hinder the progress of supervised conversational search methods. Consequently, researchers are focusing more on zero-shot conversational search approaches. Nevertheless, existing zero-shot methods face three primary limitations: they are not universally applicable to all retrievers, their effectiveness lacks sufficient explainability, and they struggle to resolve common conversational ambiguities caused by omission. To address these limitations, we introduce a novel Zero-shot Query Reformulation (ZeQR) framework that reformulates queries based on previous dialogue contexts without requiring supervision from conversational search data. Specifically, our framework utilizes language models designed for machine reading comprehension tasks to explicitly resolve two common ambiguities: coreference and omission, in raw queries. In comparison to existing zero-shot methods, our approach is universally applicable to any retriever without additional adaptation or indexing. It also provides greater explainability and effectively enhances query intent understanding because ambiguities are explicitly and proactively resolved. Through extensive experiments on four TREC conversational datasets, we demonstrate the effectiveness of our method, which consistently outperforms state-of-the-art baselines.

翻译：随着语音助手普及度的持续攀升，对话式搜索在信息检索领域受到越来越多的关注。然而，对话式搜索中的数据稀疏问题严重阻碍了有监督对话式搜索方法的发展。因此，研究者们更加关注零样本对话式搜索方法。尽管已有零样本方法取得进展，但其仍面临三大局限：无法普遍适用于所有检索器、效果缺乏充分的可解释性、难以解决由省略引发的常见对话歧义。针对这些局限，我们提出了一种新颖的零样本查询重构（Zero-shot Query Reformulation, ZeQR）框架，该框架无需对话式搜索数据的监督即可基于先前的对话上下文重构查询。具体而言，我们的框架利用专为机器阅读理解任务设计的语言模型，显式解决原始查询中的两类常见歧义：指代消解与省略补全。相比现有零样本方法，本方法无需额外适配或索引即可通用适用于任意检索器。由于歧义被显式且主动地消解，该方法不仅具有更强的可解释性，还能有效增强查询意图理解。通过在四个TREC对话数据集上进行的大量实验，我们验证了该方法的效果，其始终优于现有最先进基线方法。