  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.
