论文标题
UNIK-QA：开放域问题的结构化和非结构化知识的统一表示
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
论文作者
论文摘要
我们研究开放域的问题，该问题通过结构化，非结构化和半结构化的知识来源，包括文本，表，列表和知识库。偏离了先前的工作，我们提出了一种统一的方法，该方法通过减少文本并应用Retriever-Reader模型来统一所有来源，该模型迄今仅限于文本源。与最新的基于图的方法相比，我们的方法将知识基本质量检查任务的结果大大提高了11分。更重要的是，我们证明，我们的统一知识（UNIK-QA）模型是一种简单但有效的方法，可以分别在3.5和2.6点上结合其他知识来源，在两个流行的问题回答基准，天然测试和2.6点的基准，天然标记和网络问题上提高最新的结果。 UNIK-QA的代码可在以下网址提供：https：//github.com/facebookresearch/unik-qa。
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.
