The retriever-reader framework is popular for open-domain question answering
(ODQA) due to its ability to use explicit knowledge. Although prior work has
sought to increase the knowledge coverage by incorporating structured knowledge
beyond text, accessing heterogeneous knowledge sources through a unified
interface remains an open question. While data-to-text generation has the
potential to serve as a universal interface for data and text, its feasibility
for downstream tasks remains largely unknown. In this work, we bridge this gap
and use the data-to-text method as a means for encoding structured knowledge
for ODQA. Specifically, we propose a verbalizer-retriever-reader framework for
ODQA over data and text where verbalized tables from Wikipedia and graphs from
Wikidata are used as augmented knowledge sources. We show that our Unified Data
and Text QA, UDT-QA, can effectively benefit from the expanded knowledge index,
leading to large gains over text-only baselines. Notably, our approach sets the
single-model state-of-the-art on Natural Questions. Furthermore, our analyses
indicate that verbalized knowledge is preferred for answer reasoning for both
adapted and hot-swap settings.

本文提出了一种基于数据和文本的统一问答框架 UDT-QA，并使用数据到文本方法将 Wiki 百科中的表格和 Wikidata 中的图表达为口语化信息，以扩展知识索引。实验证明，该方法取得了自然问答 (Single-model state-of-the-art) 的最佳成果，并表明对于调整和热插拔的两种情况，口语化知识的优先考虑可以优化答案推理。