ViDeBERTa: A powerful pre-trained language model for Vietnamese
This addresses the lack of high-performing pre-trained models for Vietnamese, a low-resource language, by providing a more efficient and effective solution for natural language understanding tasks.
The paper introduces ViDeBERTa, a pre-trained language model for Vietnamese, which achieves state-of-the-art results on tasks like part-of-speech tagging, named-entity recognition, and question answering, with ViDeBERTa_base using 86M parameters outperforming or matching PhoBERT_large with 370M parameters.
This paper presents ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture. Although many successful pre-trained language models based on Transformer have been widely proposed for the English language, there are still few pre-trained models for Vietnamese, a low-resource language, that perform good results on downstream tasks, especially Question answering. We fine-tune and evaluate our model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. The empirical results demonstrate that ViDeBERTa with far fewer parameters surpasses the previous state-of-the-art models on multiple Vietnamese-specific natural language understanding tasks. Notably, ViDeBERTa_base with 86M parameters, which is only about 23% of PhoBERT_large with 370M parameters, still performs the same or better results than the previous state-of-the-art model. Our ViDeBERTa models are available at: https://github.com/HySonLab/ViDeBERTa.