A Computational Analysis of Vagueness in Revisions of

Index

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

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