SemEval-2021 Task 12: Learning with Disagreements

Alexandra Uma, Tommaso Fornaciari, Anca Dumitrache, Tristan Miller, Jon Chamberlain, Barbara Plank, Edwin Simpson, Massimo Poesio


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.
Anthology ID:
2021.semeval-1.41
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
338–347
Language:
URL:
https://aclanthology.org/2021.semeval-1.41
DOI:
10.18653/v1/2021.semeval-1.41
Bibkey:
Cite (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.
Cite (Informal):
SemEval-2021 Task 12: Learning with Disagreements (Uma et al., SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.41.pdf
Data
CIFAR-10LabelMe