A Combination of BERT and Transformer for Vietnamese Spelling Correction

TL;DR

This paper proposes a novel combination of BERT and Transformer architectures tailored for Vietnamese spelling correction, achieving superior performance over existing methods and tools like Google Docs Spell Checker.

Contribution

It introduces a new Vietnamese spelling correction model combining BERT with Transformer architecture, filling a gap in NLP resources for Vietnamese.

Findings

Model outperforms existing approaches

Achieves 86.24 BLEU score

Surpasses Google Docs Spell Checking tool

Abstract

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.