In this paper we present a new dataset and user simulator e-QRAQ (explainable
Query, Reason, and Answer Question) which tests an Agent's ability to read an
ambiguous text; ask questions until it can answer a challenge question; and
explain the reasoning behind its questions and answer. The User simulator
provides the Agent with a short, ambiguous story and a challenge question about
the story. The story is ambiguous because some of the entities have been
replaced by variables. At each turn the Agent may ask for the value of a
variable or try to answer the challenge question. In response the User
simulator provides a natural language explanation of why the Agent's query or
answer was useful in narrowing down the set of possible answers, or not. To
demonstrate one potential application of the e-QRAQ dataset, we train a new
neural architecture based on End-to-End Memory Networks to successfully
generate both predictions and partial explanations of its current understanding
of the problem. We observe a strong correlation between the quality of the
prediction and explanation.

本文中，我们提出了一个新的数据集和用户模拟器 e-QRAQ（可解释的查询、推理和回答问题），该数据集测试了代理的读取模糊文本的能力，通过提问来回答挑战性问题并解释其问题和答案的推理。我们使用端到端记忆网络来训练一个神经结构，以成功地生成对问题的预测和部分解释，并发现预测和解释的质量之间存在着很强的相关性。