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。

学习关注于主要术语：用于开放领域问答的增强型检索器 - 阅读器模型

Learning to Attend On Essential Terms: An Enhanced Retriever-Reader  Model for Open-domain Question Answering

The recent work of Clark et al. introduces the AI2 Reasoning Challenge (ARC)
and the associated ARC dataset that partitions open domain, complex science
questions into an Easy Set and a Challenge Set. That paper includes an analysis
of 100 questions with respect to the types of knowledge and reasoning required
to answer them; however, it does not include clear definitions of these types,
nor does it offer information about the quality of the labels. We propose a
comprehensive set of definitions of knowledge and reasoning types necessary for
answering the questions in the ARC dataset. Using ten annotators and a
sophisticated annotation interface, we analyze the distribution of labels
across the Challenge Set and statistics related to them. Additionally, we
demonstrate that although naive information retrieval methods return sentences
that are irrelevant to answering the query, sufficient supporting text is often
present in the (ARC) corpus. Evaluating with human-selected relevant sentences
improves the performance of a neural machine comprehension model by 42 points.

本文提出了一套完整的科学问题解答所需的知识和推理类型定义方法，回答 ARC 数据集中的问题需要的标注信息分布和相关统计的分析，证明了人工选取的相关句子能够提高神经机器理解模型的 42 个百分点的性能。

ARC 数据集中知识、推理和语境的系统分类

A Systematic Classification of Knowledge, Reasoning, and Context within  the ARC Dataset

The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question
answering (QA) has been recently released. ARC only contains natural science
questions authored for human exams, which are hard to answer and require
advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art
QA systems fail to significantly outperform random baseline, reflecting the
difficult nature of this task. In this paper, we propose a novel framework for
answering science exam questions, which mimics human solving process in an
open-book exam. To address the reasoning challenge, we construct contextual
knowledge graphs respectively for the question itself and supporting sentences.
Our model learns to reason with neural embeddings of both knowledge graphs.
Experiments on the ARC Challenge Set show that our model outperforms the
previous state-of-the-art QA systems.

本文提出了一种新的答题框架，其使用两个上下文知识图谱中的神经嵌入来解决自然科学考试问题，该模型在 AI2 Reasoning Challenge 上表现出优异的性能。