Published November 2, 2019 | Version v1
Conference paper Open

How Transformer Revitalizes Character-based Neural Machine Translation: An Investigation on Japanese-Vietnamese Translation Systems

  • 1. University of Information and Communication Technology, TNU, Vietnam
  • 2. Institute of Anthropomatics and Robotics, KIT, Germany
  • 3. University of Engineering and Technology, VNU, Vietnam
  • 4. School of Information Science, JAIST, Japan


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 trans- lation 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.



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