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：随着语音助手的普及持续增长，对话式搜索在信息检索领域日益受到关注。然而，对话式搜索中的数据稀疏问题显著阻碍了有监督对话式搜索方法的发展。因此，研究者们更加聚焦于零样本对话式搜索方法。然而，现有零样本方法面临三个主要局限性：它们并非普遍适用于所有检索器、其有效性缺乏充分的解释性，且难以解决由省略引发的常见对话歧义。为解决这些局限性，我们提出了一种新颖的零样本查询改写（ZeQR）框架，该框架基于先前对话上下文改写查询，无需依赖对话式搜索数据的监督。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，明确解决原始查询中两类常见歧义：指代省略与内容省略。与现有零样本方法相比，我们的方法无需额外适配或索引即可普遍适用于任何检索器。由于歧义被显式且主动地解决，该方法还提供了更强的解释性，并有效增强了查询意图理解。通过在四个TREC对话数据集上的广泛实验，我们证明了方法的有效性，其结果始终优于最先进的基线模型。