ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing
Nam Nguyen, Thang Phan, Duc-Vu Nguyen, Kiet Van Nguyen · 2023
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 vELEC-TRA, 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 4 only for research purposes.