ReAct: A Review Comment Dataset for Actionability (and more)
October 02, 2022 ยท Declared Dead ยท ๐ WISE
Repo contents: LICENSE.md, README.md, processed_data.csv, raw_annotated_data.csv, unlabelled_review_corpus_52k.txt
Authors
Gautam Choudhary, Natwar Modani, Nitish Maurya
arXiv ID
2210.00443
Category
cs.CL: Computation & Language
Cross-listed
cs.LG
Citations
6
Venue
WISE
Repository
https://github.com/gtmdotme/ReAct
โญ 2
Last Checked
1 month ago
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.
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