Adam-Smith在SemEval-2023的任务4:在基于变压器的模型集合的争论中发现人类的价值

Daniel Schroter, D. Dementieva, G. Groh
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引用次数: 1

摘要

本文为SemEval-2023任务4:“识别争论背后的人类价值”提出了性能最好的方法,别名为“亚当·斯密”。该任务的目标是创建能够自动识别文本参数中的值的系统。我们训练基于变压器的模型,直到它们达到损耗最小值或f1分数最大值。通过选择一个使f1得分最大化的全局决策阈值来集成模型,可以使系统在竞争中表现最佳。基于逻辑回归叠加的集成在额外的数据集上显示出最佳性能,以评估鲁棒性(“Nahj al-Balagha”)。除了概述所提交的系统外,我们还证明了没有必要使用大型集成模型,并且可以显着减小系统大小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models
This paper presents the best-performing approach alias “Adam Smith” for the SemEval-2023 Task 4: “Identification of Human Values behind Arguments”. The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness (“Nahj al-Balagha”). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced.
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