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。