@inproceedings{dycke-etal-2023-overview,
title = "Overview of {P}rag{T}ag-2023: Low-Resource Multi-Domain Pragmatic Tagging of Peer Reviews",
author = "Dycke, Nils and
Kuznetsov, Ilia and
Gurevych, Iryna",
editor = "Alshomary, Milad and
Chen, Chung-Chi and
Muresan, Smaranda and
Park, Joonsuk and
Romberg, Julia",
booktitle = "Proceedings of the 10th Workshop on Argument Mining",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.argmining-1.21",
doi = "10.18653/v1/2023.argmining-1.21",
pages = "187--196",
abstract = "Peer review is the key quality control mechanism in science. The core component of peer review are the review reports {--} argumentative texts where the reviewers evaluate the work and make suggestions to the authors. Reviewing is a demanding expert task prone to bias. An active line of research in NLP aims to support peer review via automatic analysis of review reports. This research meets two key challenges. First, NLP to date has focused on peer reviews from machine learning conferences. Yet, NLP models are prone to domain shift and might underperform when applied to reviews from a new research community. Second, while some venues make their reviewing processes public, peer reviewing data is generally hard to obtain and expensive to label. Approaches to low-data NLP processing for peer review remain under-investigated. Enabled by the recent release of open multi-domain corpora of peer reviews, the PragTag-2023 Shared Task explored the ways to increase domain robustness and address data scarcity in pragmatic tagging {--} a sentence tagging task where review statements are classified by their argumentative function. This paper describes the shared task, outlines the participating systems, and summarizes the results.",
}
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<abstract>Peer review is the key quality control mechanism in science. The core component of peer review are the review reports – argumentative texts where the reviewers evaluate the work and make suggestions to the authors. Reviewing is a demanding expert task prone to bias. An active line of research in NLP aims to support peer review via automatic analysis of review reports. This research meets two key challenges. First, NLP to date has focused on peer reviews from machine learning conferences. Yet, NLP models are prone to domain shift and might underperform when applied to reviews from a new research community. Second, while some venues make their reviewing processes public, peer reviewing data is generally hard to obtain and expensive to label. Approaches to low-data NLP processing for peer review remain under-investigated. Enabled by the recent release of open multi-domain corpora of peer reviews, the PragTag-2023 Shared Task explored the ways to increase domain robustness and address data scarcity in pragmatic tagging – a sentence tagging task where review statements are classified by their argumentative function. This paper describes the shared task, outlines the participating systems, and summarizes the results.</abstract>
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%0 Conference Proceedings
%T Overview of PragTag-2023: Low-Resource Multi-Domain Pragmatic Tagging of Peer Reviews
%A Dycke, Nils
%A Kuznetsov, Ilia
%A Gurevych, Iryna
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F dycke-etal-2023-overview
%X Peer review is the key quality control mechanism in science. The core component of peer review are the review reports – argumentative texts where the reviewers evaluate the work and make suggestions to the authors. Reviewing is a demanding expert task prone to bias. An active line of research in NLP aims to support peer review via automatic analysis of review reports. This research meets two key challenges. First, NLP to date has focused on peer reviews from machine learning conferences. Yet, NLP models are prone to domain shift and might underperform when applied to reviews from a new research community. Second, while some venues make their reviewing processes public, peer reviewing data is generally hard to obtain and expensive to label. Approaches to low-data NLP processing for peer review remain under-investigated. Enabled by the recent release of open multi-domain corpora of peer reviews, the PragTag-2023 Shared Task explored the ways to increase domain robustness and address data scarcity in pragmatic tagging – a sentence tagging task where review statements are classified by their argumentative function. This paper describes the shared task, outlines the participating systems, and summarizes the results.
%R 10.18653/v1/2023.argmining-1.21
%U https://aclanthology.org/2023.argmining-1.21
%U https://doi.org/10.18653/v1/2023.argmining-1.21
%P 187-196
Markdown (Informal)
[Overview of PragTag-2023: Low-Resource Multi-Domain Pragmatic Tagging of Peer Reviews](https://aclanthology.org/2023.argmining-1.21) (Dycke et al., ArgMining-WS 2023)
ACL