论文标题
通过多种培训策略的人工文本检测
Artificial Text Detection with Multiple Training Strategies
论文作者
论文摘要
随着深度学习的迅速促进，生成模型创建的人工文本通常在新闻和社交媒体中使用。但是，可以滥用此类模型来产生产品评论，虚假新闻，甚至是假政治内容。本文提出了一个解决方案，以对话共享任务2022（RUATD 2022）中的俄罗斯人工文本检测，以区分列表中的哪种模型来生成此文本。我们介绍了Deberta预训练的语言模型，并具有多种培训策略，以实现这项共同的任务。在RUATD数据集上进行的广泛实验验证了我们提出的方法的有效性。此外，我们的提交在RUATD 2022（多级）的评估阶段排名第二。
As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media. However, such models can be abused to generate product reviews, fake news, and even fake political content. The paper proposes a solution for the Russian Artificial Text Detection in the Dialogue shared task 2022 (RuATD 2022) to distinguish which model within the list is used to generate this text. We introduce the DeBERTa pre-trained language model with multiple training strategies for this shared task. Extensive experiments conducted on the RuATD dataset validate the effectiveness of our proposed method. Moreover, our submission ranked second place in the evaluation phase for RuATD 2022 (Multi-Class).
