Conversational Query Reformulation (CQR) has significantly advanced in addressing the challenges of conversational search, particularly those stemming from the latent user intent and the need for historical context. Recent works aimed to boost the performance of CRQ through alignment. However, they are designed for one specific retrieval system, which potentially results in poor generalization. To overcome this limitation, we present a novel framework AdaCQR. By aligning reformulation models with both term-based and semantic-based retrieval systems, AdaCQR enhances the generalizability of information-seeking queries across diverse retrieval environments through a dual-phase training strategy. We also developed two effective approaches for acquiring superior labels and diverse input candidates, boosting the efficiency and robustness of the framework. Experimental evaluations on the TopiOCQA and QReCC datasets demonstrate that AdaCQR significantly outperforms existing methods, offering both quantitative and qualitative improvements in conversational query reformulation.

翻译：对话式查询重构（CQR）在应对对话式搜索的挑战方面取得了显著进展，特别是针对隐含用户意图和历史上下文需求所带来的问题。近期研究致力于通过对齐技术提升CQR的性能，但这些方法通常针对特定检索系统设计，可能导致泛化能力不足。为克服这一局限，本文提出新型框架AdaCQR。该框架通过将重构模型与基于词项的检索系统和基于语义的检索系统同时对齐，采用双阶段训练策略，增强了信息检索查询在不同检索环境中的泛化能力。我们还开发了两种有效方法用于获取优质标签和多样化输入候选，从而提升框架的效率和鲁棒性。在TopiOCQA和QReCC数据集上的实验评估表明，AdaCQR显著优于现有方法，在对话式查询重构任务中实现了定量与定质的双重提升。