SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts

Michael Roth, Talita Anthonio, Anna Sauer
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引用次数: 12

Abstract

We describe SemEval-2022 Task 7, a shared task on rating the plausibility of clarifications in instructional texts. The dataset for this task consists of manually clarified how-to guides for which we generated alternative clarifications and collected human plausibility judgements. The task of participating systems was to automatically determine the plausibility of a clarification in the respective context. In total, 21 participants took part in this task, with the best system achieving an accuracy of 68.9%. This report summarizes the results and findings from 8 teams and their system descriptions. Finally, we show in an additional evaluation that predictions by the top participating team make it possible to identify contexts with multiple plausible clarifications with an accuracy of 75.2%.
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任务7:识别教学文本中隐含和未明确短语的合理解释
我们描述了SemEval-2022任务7,这是一个评估教学文本中澄清的合理性的共享任务。此任务的数据集由手动澄清的操作指南组成,我们为此生成了替代澄清并收集了人类的合理性判断。各参与系统的任务是在各自的情况下自动确定澄清的合理性。总共有21名参与者参加了这项任务,最好的系统达到了68.9%的准确率。本报告总结了来自8个团队的结果和发现以及他们的系统描述。最后,我们在一项额外的评估中表明,顶级参与团队的预测使识别具有多种合理解释的上下文成为可能,准确率为75.2%。
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SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts Mao-Zedong at SemEval-2023 Task 4: Label Represention Multi-Head Attention Model with Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions
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