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.

通过对 WikiHow 教学文章的修订版本进行分析，本文研究了指导指令中的模糊性，并采用神经模型的配对排名任务，展示了对指令版本的改进。

教学文本修订中的模糊性的计算分析

A Computational Analysis of Vagueness in Revisions of Instructional  Texts

Procedures are inherently hierarchical. To "make videos", one may need to
"purchase a camera", which in turn may require one to "set a budget". While
such hierarchical knowledge is critical for reasoning about complex procedures,
most existing work has treated procedures as shallow structures without
modeling the parent-child relation. In this work, we attempt to construct an
open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow, a
website containing more than 110k instructional articles, each documenting the
steps to carry out a complex procedure. To this end, we develop a simple and
efficient method that links steps (e.g., "purchase a camera") in an article to
other articles with similar goals (e.g., "how to choose a camera"), recursively
constructing the KB. Our method significantly outperforms several strong
baselines according to automatic evaluation, human judgment, and application to
downstream tasks such as instructional video retrieval.
A demo with partial data can be found at this https URL.
The code and the data are at this https URL.

本研究旨在构建一个基于 wihiHow 的、开放的、分层的程序知识库，通过开发一个简单且有效的方法，将文章中的步骤链接到具有类似目标的其他文章（如何选择相机），递归构建知识库，并在自动评估、人类判断和应用到下游任务（如视频检索）方面显著优于几个强基线。