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A Computational Analysis of Vagueness in Revisions of Instructional Texts
Computer ScienceProceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

A Computational Analysis of Vagueness in Revisions of Instructional Texts

A. Debnath and M. Roth

This research by Alok Debnath and Michael Roth dives into the intricacies of vagueness in instructional texts from the WikiHowToImprove dataset. By analyzing edits involving vagueness and developing a novel neural model to enhance clarity in instructions, they demonstrate significant advancements over existing techniques. Tune in to discover these insightful findings!... show more
Abstract
WikiHow is an open-domain repository of instructional articles for a variety of tasks, which can be revised by users. In this paper, we extract pairwise versions of an instruction before and after a revision was made. Starting from a noisy dataset of revision histories, we specifically extract and analyze edits that involve cases of vagueness in instructions. We further investigate the ability of a neural model to distinguish between two versions of an instruction in our data by adopting a pairwise ranking task from previous work and showing improvements over existing baselines.
Publisher
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Published On
Apr 19, 2021
Authors
Alok Debnath, Michael Roth
Tags
vaguenessinstructional textsWikiHowToImproveneural modelpairwise rankingtext clarityedits
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