SemEval-2021 Task 12: Learning with Disagreements
SemEval-2021 Task 12: Learning with Disagreements
复制标题
SemEval-2021 任务 12：带着分歧学习
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Massimo Poesio
中科院分区：
文献类型：
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作者：
Alexandra Uma;Tommaso Fornaciari;Anca Dumitrache;Tristan Miller;Jon P. Chamberlain;Barbara Plank;Edwin Simpson;Massimo Poesio
编码人员之间的分歧在几乎所有数据集中都在自然语言处理和计算机视觉中判断的所有数据集无处不在。但是，大多数监督的机器学习方法都假定每个项目都存在一个首选解释，这充其量是理想化的。 Semeval-2021共享的有关与分歧的学习的共享任务（Le-Wi-Di）是为从包含多个数据的数据学习的方法提供统一的测试框架，这些方法涵盖了涵盖有关包含有关分歧的信息的数据集解释语言和分类图像。在本文中，我们描述了共同的任务及其结果。
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.
DOI：
10.1109/cvpr.2018.00587
发表时间：
2018-03
期刊：
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
影响因子：
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作者：
Michael Firman;N. Campbell;L. Agapito;G. Brostow
通讯作者：
Michael Firman;N. Campbell;L. Agapito;G. Brostow