The integration of external personalized context information into document-grounded conversational systems has significant potential business value, but has not been well-studied. Motivated by the concept of personalized context-aware document-grounded conversational systems, we introduce the task of context-aware passage retrieval. We also construct a dataset specifically curated for this purpose. We describe multiple baseline systems to address this task, and propose a novel approach, Personalized Context-Aware Search (PCAS), that effectively harnesses contextual information during passage retrieval. Experimental evaluations conducted on multiple popular dense retrieval systems demonstrate that our proposed approach not only outperforms the baselines in retrieving the most relevant passage but also excels at identifying the pertinent context among all the available contexts. We envision that our contributions will serve as a catalyst for inspiring future research endeavors in this promising direction.

翻译：将外部个性化上下文信息整合到基于文档的对话系统中具有显著的潜在商业价值，但尚未得到充分研究。受个性化上下文感知文档对话系统概念的启发，我们提出了上下文感知段落检索任务，并为此专门构建了一个数据集。我们描述了解决该任务的多个基线系统，并提出了一种新颖的方法——个性化上下文感知搜索（PCAS），该方法能在段落检索过程中有效利用上下文信息。在多种主流密集检索系统上进行的实验评估表明，我们提出的方法不仅在检索最相关段落方面优于基线系统，而且在所有可用上下文中准确识别相关上下文方面也表现突出。我们预期，我们的贡献将为这一有前景的方向激发未来的研究工作提供催化剂。