Adam-Smith在SemEval-2023任务4：基于Transformer模型集成发现论证中的人类价值观
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models
AI总结：
本文提出Adam Smith方法，通过集成Transformer模型并优化全局决策阈值，在SemEval-2023任务4中识别论证中的人类价值观，取得最佳性能，并证明系统可大幅精简。
摘要：
本文介绍了在SemEval-2023任务4：“论证背后人类价值观的识别”中表现最佳的方法，名为“Adam Smith”。该任务的目标是创建能够自动识别文本论证中价值观的系统。我们训练基于Transformer的模型，直到它们达到损失最小值或F1分数最大值。通过选择一个全局决策阈值来最大化F1分数，对模型进行集成，从而在竞赛中产生了表现最佳的系统。基于逻辑回归堆叠的集成在用于评估鲁棒性的额外数据集（“Nahj al-Balagha”）上表现出最佳性能。除了概述所提交的系统外，我们还证明了使用大型集成模型并非必要，系统规模可以显著减小。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
