@inproceedings{uma-etal-2021-semeval,
title = "{S}em{E}val-2021 Task 12: Learning with Disagreements",
author = "Uma, Alexandra and
Fornaciari, Tommaso and
Dumitrache, Anca and
Miller, Tristan and
Chamberlain, Jon and
Plank, Barbara and
Simpson, Edwin and
Poesio, Massimo",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.41",
doi = "10.18653/v1/2021.semeval-1.41",
pages = "338--347",
abstract = "Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.",
}
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<abstract>Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.</abstract>
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%0 Conference Proceedings
%T SemEval-2021 Task 12: Learning with Disagreements
%A Uma, Alexandra
%A Fornaciari, Tommaso
%A Dumitrache, Anca
%A Miller, Tristan
%A Chamberlain, Jon
%A Plank, Barbara
%A Simpson, Edwin
%A Poesio, Massimo
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F uma-etal-2021-semeval
%X Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.
%R 10.18653/v1/2021.semeval-1.41
%U https://aclanthology.org/2021.semeval-1.41
%U https://doi.org/10.18653/v1/2021.semeval-1.41
%P 338-347
Markdown (Informal)
[SemEval-2021 Task 12: Learning with Disagreements](https://aclanthology.org/2021.semeval-1.41) (Uma et al., SemEval 2021)
ACL
- Alexandra Uma, Tommaso Fornaciari, Anca Dumitrache, Tristan Miller, Jon Chamberlain, Barbara Plank, Edwin Simpson, and Massimo Poesio. 2021. SemEval-2021 Task 12: Learning with Disagreements. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 338–347, Online. Association for Computational Linguistics.