In this paper, we present BARTpho with two versions BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and the pre-training scheme of the sequence-to-sequence denoising autoencoder BART, thus especially suitable for generative NLP tasks. We conduct experiments to compare our BARTpho with its competitor mBART on a downstream task of Vietnamese text summarization and show that: in both automatic and human evaluations, BARTpho outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future research and applications of generative Vietnamese NLP tasks. Our BARTpho models are publicly available at: https://github.com/VinAIResearch/BARTpho
翻译:在本文中,我们介绍BARTpho的两个版本,即BARTFO的两个版本的BARTFO和BARTFO-Word,这是第一个为越南人事先培训的公共大规模单语单语序列序列到序列模型。BARTFO使用“大”结构和AART序列到序列取消自动电算器BART的训练前计划,因此特别适合具有基因特征的NLP任务。我们进行了实验,将我们的BARTFO与其竞争对手MBART就越南文本总结的下游任务进行比较,并表明:在自动和人文评估中,BARTF超过强的基线MBART,改进艺术现状。我们释放BARTFO,以促进未来研究和应用具有基因特征的越南NLP任务。我们的BARTFO模型在https://github.com/VinAIResearch/BARTPO上公开提供。