ReAct: A Review Comment Dataset for Actionability (and more)

TL;DR

This paper introduces ReAct, a new annotated dataset of review comments from OpenReview, focusing on their actionability and types, to facilitate research in comment analysis and classification.

Contribution

The paper presents a novel, publicly available dataset of review comments with annotations on actionability and comment type, along with baseline classification benchmarks.

Findings

Dataset contains diverse review comments with high annotation quality

Baseline models achieve moderate classification performance

Analysis reveals properties and challenges of comment classification

Abstract

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.