IterCQR：检索引导的迭代式对话查询重写
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance
AI总结：
本文提出IterCQR，利用IR信号作为奖励迭代训练CQR模型，在无需人工改写的情况下生成上下文完备的独立查询，并在多个数据集及低资源、跨数据集泛化场景中取得SOTA效果。
摘要：
对话式搜索旨在检索包含必要信息的段落，以回答多轮对话中的查询。在对话式搜索中，为了有效利用现成的检索器，必须将依赖上下文的对话查询重写为独立形式。以往的对话查询重写方法通常依赖人工标注的改写。然而，这些人工构建的查询往往导致次优的检索性能，并且收集成本高昂。为应对这些挑战，我们提出了迭代式对话查询重写（Iterative Conversational Query Reformulation，IterCQR），这是一种不依赖人工改写即可执行查询重写的方法。IterCQR通过直接利用信息检索（IR）信号作为奖励，迭代训练对话查询重写（CQR）模型。我们的IterCQR训练引导CQR模型，使生成的查询包含先前对话上下文中的必要信息。所提出的方法在两个广泛使用的数据集上展现出最先进性能，证明了其在稀疏检索器和稠密检索器上的有效性。此外，IterCQR在未见过数据集上的泛化以及低资源场景等挑战性设置中也表现出优越性能。
英文摘要：
Conversational search aims to retrieve passages containing essential information to answer queries in a multi-turn conversation. In conversational search, reformulating context-dependent conversational queries into stand-alone forms is imperative to effectively utilize off-the-shelf retrievers. Previous methodologies for conversational query reformulation frequently depend on human-annotated rewrites. However, these manually crafted queries often result in sub-optimal retrieval performance and require high collection costs. To address these challenges, we propose Iterative Conversational Query Reformulation (IterCQR), a methodology that conducts query reformulation without relying on human rewrites. IterCQR iteratively trains the conversational query reformulation (CQR) model by directly leveraging information retrieval (IR) signals as a reward. Our IterCQR training guides the CQR model such that generated queries contain necessary information from the previous dialogue context. Our proposed method shows state-of-the-art performance on two widely-used datasets, demonstrating its effectiveness on both sparse and dense retrievers. Moreover, IterCQR exhibits superior performance in challenging settings such as generalization on unseen datasets and low-resource scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
