UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior work, we propose a unifying approach that homogenizes all sources by reducing them to text and applies the retriever-reader model which has so far been limited to text sources only. Our approach greatly improves the results on knowledge-base QA tasks by 11 points, compared to latest graph-based methods. More importantly, we demonstrate that our unified knowledge (UniK-QA) model is a simple and yet effective way to combine heterogeneous sources of knowledge, advancing the state-of-the-art results on two popular question answering benchmarks, NaturalQuestions and WebQuestions, by 3.5 and 2.6 points, respectively. The code of UniK-QA is available at: https://github.com/facebookresearch/UniK-QA.
PDF Abstract Findings (NAACL) 2022 PDF Findings (NAACL) 2022 Abstract
Natural Questions
TriviaQA
WebQuestions
TQA
Results from the Paper
Ranked #1 on
Open-Domain Question Answering
on WebQuestions
(using extra training data)
