Neural Machine Translation of Logographic Language Using Sub-character Level Information

Longtu Zhang, Mamoru Komachi


Abstract
Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units. However, important differences between languages with logographic and alphabetic writing systems have long been overlooked. This study focuses on these differences and uses a simple approach to improve the performance of NMT systems utilizing decomposed sub-character level information for logographic languages. Our results indicate that our approach not only improves the translation capabilities of NMT systems between Chinese and English, but also further improves NMT systems between Chinese and Japanese, because it utilizes the shared information brought by similar sub-character units.
Anthology ID:
W18-6303
Volume:
Proceedings of the Third Conference on Machine Translation: Research Papers
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
17–25
Language:
URL:
https://aclanthology.org/W18-6303
DOI:
10.18653/v1/W18-6303
Bibkey:
Cite (ACL):
Longtu Zhang and Mamoru Komachi. 2018. Neural Machine Translation of Logographic Language Using Sub-character Level Information. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 17–25, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Neural Machine Translation of Logographic Language Using Sub-character Level Information (Zhang & Komachi, WMT 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6303.pdf
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