Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering

TL;DR

This paper introduces a hybrid approach for open-domain question answering that combines textual and tabular evidence, generating either direct answers or SQL queries to improve accuracy and reasoning capabilities.

Contribution

It is the first to apply Text2SQL to ODQA, integrating structured database queries with textual evidence for enhanced answer generation.

Findings

Outperforms baseline models on multiple ODQA datasets.

Achieves state-of-the-art on OpenSQuAD with T5-base.

SQL query generation improves handling of complex reasoning questions.

Abstract

The current state-of-the-art generative models for open-domain question answering (ODQA) have focused on generating direct answers from unstructured textual information. However, a large amount of world's knowledge is stored in structured databases, and need to be accessed using query languages such as SQL. Furthermore, query languages can answer questions that require complex reasoning, as well as offering full explainability. In this paper, we propose a hybrid framework that takes both textual and tabular evidence as input and generates either direct answers or SQL queries depending on which form could better answer the question. The generated SQL queries can then be executed on the associated databases to obtain the final answers. To the best of our knowledge, this is the first paper that applies Text2SQL to ODQA tasks. Empirically, we demonstrate that on several ODQA datasets, the hybrid methods consistently outperforms the baseline models that only take homogeneous input by a large margin. Specifically we achieve state-of-the-art performance on OpenSQuAD dataset using a T5-base model. In a detailed analysis, we demonstrate that the being able to generate structural SQL queries can always bring gains, especially for those questions that requires complex reasoning.