A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks -- post-hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.

QED是一个基于语言学的可扩展框架，为问答提供解释，提高了调试能力、扩展性和信任度，可以根据规范的语义概念如引用相等性、句子性和蕴含关系说明问题和答案之间的关系，进行问波应答和解释生成的试验表明，训练涉及QED数据的模型可以显著提高问答能力，在大量测试中，通过QED解释可以显著提高非专业用户改正强神经基线所犯错误的能力。

QED: 问答解释的框架和数据集