# On the Use of Machine Translation-Based Approaches for Vietnamese Diacritic Restoration

arXiv: https://arxiv.org/abs/1709.07104 · HTML: https://mlsanity.com/paper/1709.07104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Hierarchical Transformer Encoders for Vietnamese Spelling Correction](https://mlsanity.com/paper/2105.13578/) (2021)

## Abstract (arXiv)

This paper presents an empirical study of two machine translation-based approaches for Vietnamese diacritic restoration problem, including phrase-based and neural-based machine translation models. This is the first work that applies neural-based machine translation method to this problem and gives a thorough comparison to the phrase-based machine translation method which is the current state-of-the-art method for this problem. On a large dataset, the phrase-based approach has an accuracy of 97.32% while that of the neural-based approach is 96.15%. While the neural-based method has a slightly lower accuracy, it is about twice faster than the phrase-based method in terms of inference speed. Moreover, neural-based machine translation method has much room for future improvement such as incorporating pre-trained word embeddings and collecting more training data.