QED: A Framework and Dataset for Explanations in Question Answering

• Published in: Transactions of the Association for Computational Linguistics Vol. 9; pp. 790 - 806
• Main Authors: Lamm, Matthew, Palomaki, Jennimaria, Alberti, Chris, Andor, Daniel, Choi, Eunsol, Soares, Livio Baldini, Collins, Michael
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 02.08.2021; MIT Press Journals, The; The MIT Press
• Subjects: Computational linguistics; Datasets; Entailment; Extensibility; Formal semantics; Quantum electrodynamics; Question answer sequences; Questions
• ISSN: 2307-387X; 2307-387X
• DOI: 10.1162/tacl_a_00398

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.