论文标题
使用变压器模型集合检测生成的科学论文
Detecting Generated Scientific Papers using an Ensemble of Transformer Models
论文作者
论文摘要
本文描述了为DAGPAP22共享任务开发的神经模型，该任务在第三次有关学术文档处理的研讨会上。该共享的任务针对自动检测生成的科学论文。我们的工作着重于比较不同的基于变压器的模型，并使用其他数据集和技术来处理不平衡的类。作为最后的提交，我们利用了Scibert，Roberta和Deberta的合奏，并使用随机过采样技术进行了微调。我们的模型在F1得分方面达到了99.24％。官方评估结果使我们的系统排名第三。
The paper describes neural models developed for the DAGPap22 shared task hosted at the Third Workshop on Scholarly Document Processing. This shared task targets the automatic detection of generated scientific papers. Our work focuses on comparing different transformer-based models as well as using additional datasets and techniques to deal with imbalanced classes. As a final submission, we utilized an ensemble of SciBERT, RoBERTa, and DeBERTa fine-tuned using random oversampling technique. Our model achieved 99.24% in terms of F1-score. The official evaluation results have put our system at the third place.
