What-if I ask you to explain: Explaining the effects of perturbations in procedural text

Dheeraj Rajagopal, Niket Tandon, Peter Clark, Bhavana Dalvi, Eduard Hovy


Abstract
Our goal is to explain the effects of perturbations in procedural text, e.g., given a passage describing a rabbit’s life cycle, explain why illness (the perturbation) may reduce the rabbit population (the effect). Although modern systems are able to solve the original prediction task well (e.g., illness results in less rabbits), the explanation task - identifying the causal chain of events from perturbation to effect - remains largely unaddressed, and is the goal of this research. We present QUARTET, a system that constructs such explanations from paragraphs, by modeling the explanation task as a multitask learning problem. QUARTET constructs explanations from the sentences in the procedural text, achieving ~18 points better on explanation accuracy compared to several strong baselines on a recent process comprehension benchmark. On an end task on this benchmark, we show a surprising finding that good explanations do not have to come at the expense of end task performance, in fact leading to a 7% F1 improvement over SOTA.
Anthology ID:
2020.findings-emnlp.300
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3345–3355
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.300
DOI:
10.18653/v1/2020.findings-emnlp.300
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.300.pdf