How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context

TL;DR

This paper introduces a new task of context-aware passage retrieval that leverages personalized external context, presents a dataset, and proposes a novel method that outperforms baselines in relevance and context identification.

Contribution

The paper defines the task of context-aware passage retrieval, creates a dedicated dataset, and proposes the Personalized Context-Aware Search (PCAS) method that effectively uses contextual information.

Findings

PCAS outperforms baseline retrieval systems in relevance.

PCAS excels at identifying pertinent context.

Experimental results validate the effectiveness of PCAS.

Abstract

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.