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.

翻译：在文件的演变过程中,审查评论起着重要作用。对于一个大的文件来说,审查评论的数量可能很大,使作者难以迅速掌握评论的内容。重要的是确定评论的性质,以确定哪些评论需要文件作者采取某些行动,同时确定这些评论的类型。在本文中,我们介绍附加说明的审查评论数据集ReAct。审查评论来自OpenReview网站。我们对这些审查的多方源说明的可操作性和评论类型。我们分析数据集的特性并验证说明的质量。我们向研究界公布数据集(https://github.com/gtmdotme/ReAct),作为主要贡献。我们还以分类任务的标准基准来衡量我们的数据,并分析其业绩。