Argument Mining for Understanding Peer Reviews

Xinyu Hua, Mitko Nikolov, Nikhil Badugu, Lu Wang


Abstract
Peer-review plays a critical role in the scientific writing and publication ecosystem. To assess the efficiency and efficacy of the reviewing process, one essential element is to understand and evaluate the reviews themselves. In this work, we study the content and structure of peer reviews under the argument mining framework, through automatically detecting (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). We first collect 14.2K reviews from major machine learning and natural language processing venues. 400 reviews are annotated with 10,386 propositions and corresponding types of Evaluation, Request, Fact, Reference, or Quote. We then train state-of-the-art proposition segmentation and classification models on the data to evaluate their utilities and identify new challenges for this new domain, motivating future directions for argument mining. Further experiments show that proposition usage varies across venues in amount, type, and topic.
Anthology ID:
N19-1219
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2131–2137
Language:
URL:
https://www.aclweb.org/anthology/N19-1219
DOI:
10.18653/v1/N19-1219
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1219.pdf
Supplementary:
 N19-1219.Supplementary.pdf
Video:
 https://vimeo.com/355808962