Artificial Text Detection with Multiple Training Strategies

Autor:	Li, Bin, Weng, Yixuan, Song, Qiya, Deng, Hanjun
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Zdroj:	Computational linguistics and intellectual technologies: Papers from the annual conference Dialogue. 2022
Druh dokumentu:	Working Paper
Popis:	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). Comment: Accepted by Dialogue-2022 Conference. 7 pages, 2 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.05194 Zobrazit plný text záznamu View this record from Arxiv