How Can Context Help? Exploring Joint Retrieval of Passage and
  Personalized Context
Hui Wan Hongkang Li Songtao Lu Xiaodong Cui Marina Danilevsky
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.