BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
This addresses the problem of limited generative NLP resources for Vietnamese, enabling better performance in tasks like summarization and text restoration, though it is incremental as it adapts an existing method to a new language.
The authors tackled the lack of large-scale monolingual sequence-to-sequence models for Vietnamese by introducing BARTpho, which outperformed the strong baseline mBART in text summarization, capitalization, and punctuation restoration tasks, improving the state-of-the-art in both automatic and human evaluations.
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 it is 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 further evaluate and compare BARTpho and mBART on the Vietnamese capitalization and punctuation restoration tasks and also find that BARTpho is more effective than mBART on these two tasks. We publicly release BARTpho to facilitate future research and applications of generative Vietnamese NLP tasks. Our BARTpho models are available at https://github.com/VinAIResearch/BARTpho