SemEval-2021 Task 12: Learning with Disagreements
| Autor: | Jon Chamberlain, Tommaso Fornaciari, Massimo Poesio, Barbara Plank, Anca Dumitrache, Alexandra Uma, Edwin Simpson, Tristan Miller |
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| Rok vydání: | 2021 |
| Předmět: |
Interpretation (logic)
business.industry Computer science LEARNING WITH DISAGREEMENTS LE-WI-DI SHARED TASK computer.software_genre SemEval Task (project management) LEARNING WITH DISAGREEMENTS SHARED TASK ComputingMethodologies_PATTERNRECOGNITION Idealization Artificial intelligence LE-WI-DI business computer Natural language processing |
| Zdroj: | Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) SemEval@ACL/IJCNLP |
| DOI: | 10.18653/v1/2021.semeval-1.41 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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