A Combination of BERT and Transformer for Vietnamese Spelling Correction
This addresses spelling correction for Vietnamese language users, but it is incremental as it applies existing methods to a new language.
The paper tackled Vietnamese spelling correction by combining BERT and Transformer, achieving an 86.24 BLEU score and outperforming other approaches including Google Docs Spell Checking.
Recently, many studies have shown the efficiency of using Bidirectional Encoder Representations from Transformers (BERT) in various Natural Language Processing (NLP) tasks. Specifically, English spelling correction task that uses Encoder-Decoder architecture and takes advantage of BERT has achieved state-of-the-art result. However, to our knowledge, there is no implementation in Vietnamese yet. Therefore, in this study, a combination of Transformer architecture (state-of-the-art for Encoder-Decoder model) and BERT was proposed to deal with Vietnamese spelling correction. The experiment results have shown that our model outperforms other approaches as well as the Google Docs Spell Checking tool, achieves an 86.24 BLEU score on this task.