QED: A Framework and Dataset for Explanations in Question Answering

Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, Michael Collins


Abstract
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.
Anthology ID:
2021.tacl-1.48
Volume:
Transactions of the Association for Computational Linguistics, Volume 9
Month:
Year:
2021
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
790–806
Language:
URL:
https://aclanthology.org/2021.tacl-1.48
DOI:
10.1162/tacl_a_00398
Bibkey:
Cite (ACL):
Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, and Michael Collins. 2021. QED: A Framework and Dataset for Explanations in Question Answering. Transactions of the Association for Computational Linguistics, 9:790–806.
Cite (Informal):
QED: A Framework and Dataset for Explanations in Question Answering (Lamm et al., TACL 2021)
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PDF:
https://aclanthology.org/2021.tacl-1.48.pdf