@inproceedings{leonardelli-etal-2025-lewidi,
title = "{L}e{W}i{D}i-2025 at {NLP}erspectives: The Third Edition of the Learning with Disagreements Shared Task",
author = "Leonardelli, Elisa and
Casola, Silvia and
Peng, Siyao and
Rizzi, Giulia and
Basile, Valerio and
Fersini, Elisabetta and
Frassinelli, Diego and
Jang, Hyewon and
Pavlovic, Maja and
Plank, Barbara and
Poesio, Massimo",
editor = "Abercrombie, Gavin and
Basile, Valerio and
Frenda, Simona and
Tonelli, Sara and
Dudy, Shiran",
booktitle = "Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlperspectives-1.16/",
pages = "182--195",
ISBN = "979-8-89176-350-0",
abstract = "Many researchers have reached the conclusion that ai models should be trained to be aware of the possibility of variation and disagreement in human judgments, and evaluated as per their ability to recognize such variation. The LeWiDi series of shared tasks on Learning With Disagreements was established to promote this approach to training and evaluating ai models, by making suitable datasets more accessible and by developing evaluation methods. The third edition of the task builds on this goal by extending the LeWiDi benchmark to four datasets spanning paraphrase identification, irony detection, sarcasm detection, and natural language inference, with labeling schemes that include not only categorical judgments as in previous editions, but ordinal judgments as well. Another novelty is that we adopt two complementary paradigms to evaluate disagreement-aware systems: the soft-label approach, in which models predict population-level distributions of judgments, and the perspectivist approach, in which models predict the interpretations of individual annotators. Crucially, we moved beyond standard metrics such as cross-entropy, and tested new evaluation metrics for the two paradigms. The task attracted diverse participation, and the results provide insights into the strengths and limitations of methods to modeling variation. Together, these contributions strengthen LeWiDi as a framework and provide new resources, benchmarks, and findings to support the development of disagreement-aware technologies."
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<abstract>Many researchers have reached the conclusion that ai models should be trained to be aware of the possibility of variation and disagreement in human judgments, and evaluated as per their ability to recognize such variation. The LeWiDi series of shared tasks on Learning With Disagreements was established to promote this approach to training and evaluating ai models, by making suitable datasets more accessible and by developing evaluation methods. The third edition of the task builds on this goal by extending the LeWiDi benchmark to four datasets spanning paraphrase identification, irony detection, sarcasm detection, and natural language inference, with labeling schemes that include not only categorical judgments as in previous editions, but ordinal judgments as well. Another novelty is that we adopt two complementary paradigms to evaluate disagreement-aware systems: the soft-label approach, in which models predict population-level distributions of judgments, and the perspectivist approach, in which models predict the interpretations of individual annotators. Crucially, we moved beyond standard metrics such as cross-entropy, and tested new evaluation metrics for the two paradigms. The task attracted diverse participation, and the results provide insights into the strengths and limitations of methods to modeling variation. Together, these contributions strengthen LeWiDi as a framework and provide new resources, benchmarks, and findings to support the development of disagreement-aware technologies.</abstract>
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%0 Conference Proceedings
%T LeWiDi-2025 at NLPerspectives: The Third Edition of the Learning with Disagreements Shared Task
%A Leonardelli, Elisa
%A Casola, Silvia
%A Peng, Siyao
%A Rizzi, Giulia
%A Basile, Valerio
%A Fersini, Elisabetta
%A Frassinelli, Diego
%A Jang, Hyewon
%A Pavlovic, Maja
%A Plank, Barbara
%A Poesio, Massimo
%Y Abercrombie, Gavin
%Y Basile, Valerio
%Y Frenda, Simona
%Y Tonelli, Sara
%Y Dudy, Shiran
%S Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-350-0
%F leonardelli-etal-2025-lewidi
%X Many researchers have reached the conclusion that ai models should be trained to be aware of the possibility of variation and disagreement in human judgments, and evaluated as per their ability to recognize such variation. The LeWiDi series of shared tasks on Learning With Disagreements was established to promote this approach to training and evaluating ai models, by making suitable datasets more accessible and by developing evaluation methods. The third edition of the task builds on this goal by extending the LeWiDi benchmark to four datasets spanning paraphrase identification, irony detection, sarcasm detection, and natural language inference, with labeling schemes that include not only categorical judgments as in previous editions, but ordinal judgments as well. Another novelty is that we adopt two complementary paradigms to evaluate disagreement-aware systems: the soft-label approach, in which models predict population-level distributions of judgments, and the perspectivist approach, in which models predict the interpretations of individual annotators. Crucially, we moved beyond standard metrics such as cross-entropy, and tested new evaluation metrics for the two paradigms. The task attracted diverse participation, and the results provide insights into the strengths and limitations of methods to modeling variation. Together, these contributions strengthen LeWiDi as a framework and provide new resources, benchmarks, and findings to support the development of disagreement-aware technologies.
%U https://aclanthology.org/2025.nlperspectives-1.16/
%P 182-195
Markdown (Informal)
[LeWiDi-2025 at NLPerspectives: The Third Edition of the Learning with Disagreements Shared Task](https://aclanthology.org/2025.nlperspectives-1.16/) (Leonardelli et al., NLPerspectives 2025)
ACL
- Elisa Leonardelli, Silvia Casola, Siyao Peng, Giulia Rizzi, Valerio Basile, Elisabetta Fersini, Diego Frassinelli, Hyewon Jang, Maja Pavlovic, Barbara Plank, and Massimo Poesio. 2025. LeWiDi-2025 at NLPerspectives: The Third Edition of the Learning with Disagreements Shared Task. In Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP, pages 182–195, Suzhou, China. Association for Computational Linguistics.