PhoBERT: Pre-trained language models for Vietnamese
This addresses the problem of limited NLP resources for Vietnamese, enabling improved downstream applications and research, though it is incremental as it adapts existing pre-training methods to a new language.
The authors tackled the lack of large-scale monolingual pre-trained language models for Vietnamese by introducing PhoBERT, which outperforms the multilingual XLM-R model and achieves state-of-the-art results in tasks such as Part-of-speech tagging, Dependency parsing, Named-entity recognition, and Natural language inference.
We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. We release PhoBERT to facilitate future research and downstream applications for Vietnamese NLP. Our PhoBERT models are available at https://github.com/VinAIResearch/PhoBERT