@inproceedings{kennard-etal-2022-disapere,
title = "{DISAPERE}: A Dataset for Discourse Structure in Peer Review Discussions",
author = "Kennard, Neha Nayak and
O{'}Gorman, Tim and
Das, Rajarshi and
Sharma, Akshay and
Bagchi, Chhandak and
Clinton, Matthew and
Yelugam, Pranay Kumar and
Zamani, Hamed and
McCallum, Andrew",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.89",
doi = "10.18653/v1/2022.naacl-main.89",
pages = "1234--1249",
abstract = "At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined. We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors{'} stance towards review arguments. Further, we annotate \textit{every} review and rebuttal sentence. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.",
}
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<abstract>At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined. We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors’ stance towards review arguments. Further, we annotate every review and rebuttal sentence. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.</abstract>
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%0 Conference Proceedings
%T DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
%A Kennard, Neha Nayak
%A O’Gorman, Tim
%A Das, Rajarshi
%A Sharma, Akshay
%A Bagchi, Chhandak
%A Clinton, Matthew
%A Yelugam, Pranay Kumar
%A Zamani, Hamed
%A McCallum, Andrew
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F kennard-etal-2022-disapere
%X At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined. We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors’ stance towards review arguments. Further, we annotate every review and rebuttal sentence. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.
%R 10.18653/v1/2022.naacl-main.89
%U https://aclanthology.org/2022.naacl-main.89
%U https://doi.org/10.18653/v1/2022.naacl-main.89
%P 1234-1249
Markdown (Informal)
[DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions](https://aclanthology.org/2022.naacl-main.89) (Kennard et al., NAACL 2022)
ACL
- Neha Nayak Kennard, Tim O’Gorman, Rajarshi Das, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam, Hamed Zamani, and Andrew McCallum. 2022. DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1234–1249, Seattle, United States. Association for Computational Linguistics.