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.

个性化上下文感知的文档导向对话系统的集成具有重要的商业价值，但尚未得到充分研究。通过个性化上下文感知的文档导向对话系统的概念，我们引入了上下文感知的段落检索任务，并构建了一个专门为此目的策划的数据集。我们描述了多个基线系统来解决这个任务，并提出了一种新颖的方法，个性化上下文感知搜索（PCAS），在段落检索过程中有效利用上下文信息。在多个常见的密集检索系统上进行的实证评估表明，我们提出的方法不仅在检索相关段落方面优于基线系统，而且在识别所有可用上下文中的相关环境方面表现出色。我们预计我们的工作将成为激发未来研究在这个有前景的方向上的催化剂。

如何利用上下文？探索篇章和个性化上下文的联合检索