A lot of progress has been made to improve question answering (QA) in recent
years, but the special problem of QA over narrative book stories has not been
explored in-depth. We formulate BookQA as an open-domain QA task given its
similar dependency on evidence retrieval. We further investigate how
state-of-the-art open-domain QA approaches can help BookQA. Besides achieving
state-of-the-art on the NarrativeQA benchmark, our study also reveals the
difficulty of evidence retrieval in books with a wealth of experiments and
analysis - which necessitates future effort on novel solutions for evidence
retrieval in BookQA.

本研究探讨了如何在叙述式图书中进行开放域问题回答（QA）任务，揭示了书籍中的证据检索难度，并提出了解决方案。

图书问答中令人沮丧的证据检索

Frustratingly Hard Evidence Retrieval for QA Over Books

We present a system for answering questions based on the full text of books
(BookQA), which first selects book passages given a question at hand, and then
uses a memory network to reason and predict an answer. To improve
generalization, we pretrain our memory network using artificial questions
generated from book sentences. We experiment with the recently published
NarrativeQA corpus, on the subset of Who questions, which expect book
characters as answers. We experimentally show that BERT-based retrieval and
pretraining improve over baseline results significantly. At the same time, we
confirm that NarrativeQA is a highly challenging data set, and that there is
need for novel research in order to achieve high-precision BookQA results. We
analyze some of the bottlenecks of the current approach, and we argue that more
research is needed on text representation, retrieval of relevant passages, and
reasoning, including commonsense knowledge.

本论文介绍了一个基于纯文本书籍进行问答的系统（BookQA），使用记忆网络对问题进行推理，并对前人的工作进行了改进，包括 BERT 检索和基于书句生成的预训练。在最新的 NarrativeQA 语料库的测试中，我们证明了这种方法的可行性和需要进行更多的研究，如文本表示，相关段落的检索和推理。