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arXiv · 1709.07104

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

Thai-Hoang Pham·Xuan-Khoai Pham·Phuong Le-Hong
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Abstract

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.

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Thai-Hoang Pham, Xuan-Khoai Pham, Phuong Le-Hong. 2017-09-20. On the Use of Machine Translation-Based Approaches for Vietnamese Diacritic Restoration. https://arxiv.org/abs/1709.07104

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