Question Answering (QA), as a research field, has primarily focused on either
knowledge bases (KBs) or free text as a source of knowledge. These two sources
have historically shaped the kinds of questions that are asked over these
sources, and the methods developed to answer them. In this work, we look
towards a practical use-case of QA over user-instructed knowledge that uniquely
combines elements of both structured QA over knowledge bases, and unstructured
QA over narrative, introducing the task of multi-relational QA over personal
narrative. As a first step towards this goal, we make three key contributions:
(i) we generate and release TextWorldsQA, a set of five diverse datasets, where
each dataset contains dynamic narrative that describes entities and relations
in a simulated world, paired with variably compositional questions over that
knowledge, (ii) we perform a thorough evaluation and analysis of several
state-of-the-art QA models and their variants at this task, and (iii) we
release a lightweight Python-based framework we call TextWorlds for easily
generating arbitrary additional worlds and narrative, with the goal of allowing
the community to create and share a growing collection of diverse worlds as a
test-bed for this task.

本论文探索了一种独特的用户指导的知识源，介绍了个人叙述的多关系问答。作者生成并发布了五个数据集 (TextWorldsQA)，并在该任务上对几种最先进的问答模型及其变种进行了全面的评估和分析，以及发布了一个轻量级的 Python 框架 (TextWorlds)，用于生成任意的世界和叙述。