WikiIns: A High-Quality Dataset for Controlled Text Editing by Natural
  Language Instruction
Xiang Chen Zheng Li Xiaojun Wan
Text editing, i.e., the process of modifying or manipulating text, is a
crucial step in human writing process. In this paper, we study the problem of
controlled text editing by natural language instruction. According to a given
instruction that conveys the edit intention and necessary information, an
original draft text is required to be revised into a target text. Existing
automatically constructed datasets for this task are limited because they do
not have informative natural language instruction. The informativeness requires
the information contained in the instruction to be enough to produce the
revised text. To address this limitation, we build and release WikiIns, a
high-quality controlled text editing dataset with improved informativeness. We
first preprocess the Wikipedia edit history database to extract the raw data
(WikiIns-Raw). Then we crowdsource high-quality validation and test sets, as
well as a small-scale training set (WikiIns-Gold). With the high-quality
annotated dataset, we further propose automatic approaches to generate a
large-scale ``silver'' training set (WikiIns-Silver). Finally, we provide some
insightful analysis on our WikiIns dataset, including the evaluation results
and the edit intention analysis. Our analysis and the experiment results on
WikiIns may assist the ongoing research on text editing. The dataset, source
code and annotation guideline are available at
https://github.com/CasparSwift/WikiIns.