A Computational Analysis of Vagueness in Revisions of Instructional Texts
This work addresses the issue of vagueness in user-edited instructional texts, which is incremental as it builds on prior pairwise ranking tasks.
The paper tackled the problem of analyzing vagueness in revisions of instructional texts from WikiHow, and the result was a neural model that improved over existing baselines in distinguishing between versions of instructions.
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.