论文标题
对话框22 RUATD生成的文本检测
DIALOG-22 RuATD Generated Text Detection
论文作者
论文摘要
文本生成模型（TGM）成功地创建了与人类语言风格相匹配的文本。可以区分TGM生成的文本和人写的探测器在防止滥用TGM方面起着重要作用。 在本文中，我们描述了两个Dialog-22 RUATD任务的管道：检测生成的文本（二进制任务）和使用哪种模型的分类来生成文本（多类任务）。我们在二进制分类任务上获得了第一名，精度得分为0.82995在私人测试集上，在多类分类任务上排名第四，精度得分为0.62856，在私有测试集中。我们提出了一种基于注意机制的不同预训练模型的合奏方法。
Text Generation Models (TGMs) succeed in creating text that matches human language style reasonably well. Detectors that can distinguish between TGM-generated text and human-written ones play an important role in preventing abuse of TGM. In this paper, we describe our pipeline for the two DIALOG-22 RuATD tasks: detecting generated text (binary task) and classification of which model was used to generate text (multiclass task). We achieved 1st place on the binary classification task with an accuracy score of 0.82995 on the private test set and 4th place on the multiclass classification task with an accuracy score of 0.62856 on the private test set. We proposed an ensemble method of different pre-trained models based on the attention mechanism.
