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Abstract
This paper analyzes vagueness in revisions of instructional texts from the WikiHowToImprove dataset. The authors extract and analyze edits involving vagueness, focusing on changes in the main verb. They develop a neural model for a pairwise ranking task to distinguish between vague and clarified instructions, 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
vagueness
instructional texts
WikiHowToImprove
neural model
pairwise ranking
text clarity
edits
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