论文标题
REECT：评论评论数据集，以进行可操作（以及更多）
ReAct: A Review Comment Dataset for Actionability (and more)
论文作者
论文摘要
评论评论在文档的演变中起着重要作用。对于大量文档，评论评论的数量可能会大大，因此作者很难快速掌握评论的内容。重要的是要确定评论的性质，以确定文件作者需要采取某些行动，并确定这些评论的类型。在本文中，我们介绍了带注释的评论评论数据集React。评论评论来自OpenReview网站。我们为这些评论提供了众包注释，以进行可操作性和评论类型。我们分析数据集的属性并验证注释的质量。我们将数据集（https://github.com/gtmdotme/reaeact）发布到研究界，作为主要贡献。我们还使用标准基准进行分类任务并分析其性能的标准基准测试我们的数据。
Review comments play an important role in the evolution of documents. For a large document, the number of review comments may become large, making it difficult for the authors to quickly grasp what the comments are about. It is important to identify the nature of the comments to identify which comments require some action on the part of document authors, along with identifying the types of these comments. In this paper, we introduce an annotated review comment dataset ReAct. The review comments are sourced from OpenReview site. We crowd-source annotations for these reviews for actionability and type of comments. We analyze the properties of the dataset and validate the quality of annotations. We release the dataset (https://github.com/gtmdotme/ReAct) to the research community as a major contribution. We also benchmark our data with standard baselines for classification tasks and analyze their performance.
