English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes.

翻译：英语和中文作为资源丰富的语言，在基于Transformer的自然语言处理任务中已展现出强大发展。尽管越南拥有约1亿越南语使用者，但现有预训练模型（如PhoBERT、ViBERT和vELECTRA）虽然在通用越南语自然语言处理任务（包括词性标注和命名实体识别）上表现良好，仍难以应对越南语社交媒体任务。本文首次提出面向越南语社交媒体文本的单语预训练语言模型ViSoBERT，该模型基于XLM-R架构，在高质量、多样化的越南语社交媒体大规模语料库上完成预训练。我们进一步在越南语社交媒体文本的五个重要自然语言下游任务上探索该预训练模型：情感识别、仇恨言论检测、情感分析、垃圾评论检测及仇恨言论跨度检测。实验表明，ViSoBERT在参数规模显著更小的情况下，在多项越南语社交媒体任务上超越先前最优模型。本模型的ViSoBERT版本仅限研究用途使用。