UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering

Abstract

We study open-domain question answering with structured, unstructured and semistructured 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 retrieverreader 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 graphbased methods. More importantly, we demonstrate that our unified knowledge (UniK-QA1) 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.