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:
this https URL

该研究致力于利用结构化、非结构化和半结构化知识来源（如文本、表格、列表和知识库）进行开放域问题回答，并提出了统一的方法，将它们转化为文本并采用检索 - 阅读器模型，从而大大提高了在知识库问题回答任务上的表现，并将异构知识来源融合成一个简单且有效的统一知识模型，并在两个流行的问答基准测试中（自然问题和网络问题）继续提高最新的图形方法等级，分别为 3.5 和 2.6 点。

UniK-QA: 面向开放域问答的结构化和非结构化知识统一表示

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

Open-domain question answering remains a challenging task as it requires
models that are capable of understanding questions and answers, collecting
useful information, and reasoning over evidence. Previous work typically
formulates this task as a reading comprehension or entailment problem given
evidence retrieved from search engines. However, existing techniques struggle
to retrieve indirectly related evidence when no directly related evidence is
provided, especially for complex questions where it is hard to parse precisely
what the question asks. In this paper we propose a retriever-reader model that
learns to attend on essential terms during the question answering process. We
build (1) an essential term selector which first identifies the most important
words in a question, then reformulates the query and searches for related
evidence; and (2) an enhanced reader that distinguishes between essential terms
and distracting words to predict the answer. We evaluate our model on multiple
open-domain multiple-choice QA datasets, notably performing at the level of the
state-of-the-art on the AI2 Reasoning Challenge (ARC) dataset.

本文提出了一种检索 - 阅读器模型，该模型能够在回答问题过程中学习关注必要的词语，该模型包括一个选择器来确定问题中最重要的词语，再重新制定查询，搜索相关证据； 和一种增强阅读器来区分必要词语和分心的词语以预测答案，在多个开放域多项选择 QA 数据集上进行了评估，尤其在 AI2 推理挑战（ARC）数据集上达到了业界最高水平。