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
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2021.semeval-1.41.pdf
- Data
- CIFAR-10, CIFAR-10H, LabelMe