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        <title>Reasoning about Goals, Steps, and Temporal Ordering with WikiHow</title>
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        <namePart type="given">Li</namePart>
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    <abstract>We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations (“learn poses” is a step in the larger goal of “doing yoga”) and step-step temporal relations (“buy a yoga mat” typically precedes “learn poses”). We introduce a dataset targeting these two relations based on wikiHow, a website of instructional how-to articles. Our human-validated test set serves as a reliable benchmark for common-sense inference, with a gap of about 10% to 20% between the performance of state-of-the-art transformer models and human performance. Our automatically-generated training set allows models to effectively transfer to out-of-domain tasks requiring knowledge of procedural events, with greatly improved performances on SWAG, Snips, and Story Cloze Test in zero- and few-shot settings.</abstract>
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%0 Conference Proceedings
%T Reasoning about Goals, Steps, and Temporal Ordering with WikiHow
%A Zhang, Li
%A Lyu, Qing
%A Callison-Burch, Chris
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-reasoning
%X We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations (“learn poses” is a step in the larger goal of “doing yoga”) and step-step temporal relations (“buy a yoga mat” typically precedes “learn poses”). We introduce a dataset targeting these two relations based on wikiHow, a website of instructional how-to articles. Our human-validated test set serves as a reliable benchmark for common-sense inference, with a gap of about 10% to 20% between the performance of state-of-the-art transformer models and human performance. Our automatically-generated training set allows models to effectively transfer to out-of-domain tasks requiring knowledge of procedural events, with greatly improved performances on SWAG, Snips, and Story Cloze Test in zero- and few-shot settings.
%R 10.18653/v1/2020.emnlp-main.374
%U https://aclanthology.org/2020.emnlp-main.374
%U https://doi.org/10.18653/v1/2020.emnlp-main.374
%P 4630-4639

Markdown (Informal)

[Reasoning about Goals, Steps, and Temporal Ordering with WikiHow](https://aclanthology.org/2020.emnlp-main.374) (Zhang et al., EMNLP 2020)

• Reasoning about Goals, Steps, and Temporal Ordering with WikiHow (Zhang et al., EMNLP 2020)

ACL

• Li Zhang, Qing Lyu, and Chris Callison-Burch. 2020. Reasoning about Goals, Steps, and Temporal Ordering with WikiHow. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4630–4639, Online. Association for Computational Linguistics.