Wikipedia can be edited by anyone and thus contains various quality
sentences. Therefore, Wikipedia includes some poor-quality edits, which are
often marked up by other editors. While editors' reviews enhance the
credibility of Wikipedia, it is hard to check all edited text. Assisting in
this process is very important, but a large and comprehensive dataset for
studying it does not currently exist. Here, we propose WikiSQE, the first
large-scale dataset for sentence quality estimation in Wikipedia. Each sentence
is extracted from the entire revision history of Wikipedia, and the target
quality labels were carefully investigated and selected. WikiSQE has about 3.4
M sentences with 153 quality labels. In the experiment with automatic
classification using competitive machine learning models, sentences that had
problems with citation, syntax/semantics, or propositions were found to be more
difficult to detect. In addition, we conducted automated essay scoring
experiments to evaluate the generalizability of the dataset. We show that the
models trained on WikiSQE perform better than the vanilla model, indicating its
potential usefulness in other domains. WikiSQE is expected to be a valuable
resource for other tasks in NLP.

提出第一个 Wikipedia 句子质量评估的大规模数据集 WikiSQE，包含了约 340 万句子和 153 个质量标签，并通过机器学习模型进行了实验自动分类，显示具有引文、句法 / 语义或命题问题的句子更难以检测，该数据集在自动化的文章评分实验中表现出更好的泛化性能，并有望成为自然语言处理中其他任务的有价值的资源。