How Transformer Revitalizes Character-based Neural Machine Translation: An Investigation on Japanese-Vietnamese Translation Systems
This work addresses translation challenges for low-resource East Asian languages, but it is incremental as it applies an existing transformer method to a specific domain.
The paper tackled the problem of character-based neural machine translation for low-resource language pairs like Japanese-Vietnamese, showing that transformer architectures outperform state-of-the-art word-based recurrent systems.
While translating between East Asian languages, many works have discovered clear advantages of using characters as the translation unit. Unfortunately, traditional recurrent neural machine translation systems hinder the practical usage of those character-based systems due to their architectural limitations. They are unfavorable in handling extremely long sequences as well as highly restricted in parallelizing the computations. In this paper, we demonstrate that the new transformer architecture can perform character-based translation better than the recurrent one. We conduct experiments on a low-resource language pair: Japanese-Vietnamese. Our models considerably outperform the state-of-the-art systems which employ word-based recurrent architectures.