FHAC在GermEval 2021：识别德国有毒，引人入胜，并与集成学习事实主张的意见
FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning
摘要：
近年来，通过大型预训练神经网络模型（如BERT和ELECTRA）学习的语言表示的可用性导致了许多下游自然语言处理任务的改进。预训练模型通常在预训练目标、架构和训练数据集方面有所不同，这可能会影响下游性能。在这篇文章中，我们微调了德国BERT和德国ELECTRA模型，以识别GermEval 2021竞赛提供的Facebook数据中的有毒（子任务1），参与（子任务2）和事实声明评论（子任务3）。我们创建了这些模型的集合，并研究了分类性能是否以及如何取决于集合成员的数量及其组成。在样本外数据上，我们的最佳集成实现了0.73的宏观F1得分（对于所有子任务），子任务1，2和3的F1得分分别为0.72，0.70和0.76。
英文摘要：
The availability of language representations learned by large pretrained neural network models (such as BERT and ELECTRA) has led to improvements in many downstream Natural Language Processing tasks in recent years. Pretrained models usually differ in pretraining objectives, architectures, and datasets they are trained on which can affect downstream performance. In this contribution, we fine-tuned German BERT and German ELECTRA models to identify toxic (subtask 1), engaging (subtask 2), and fact-claiming comments (subtask 3) in Facebook data provided by the GermEval 2021 competition. We created ensembles of these models and investigated whether and how classification performance depends on the number of ensemble members and their composition. On out-of-sample data, our best ensemble achieved a macro-F1 score of 0.73 (for all subtasks), and F1 scores of 0.72, 0.70, and 0.76 for subtasks 1, 2, and 3, respectively.
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。
