Longtu Zhang


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Neural Machine Translation of Logographic Language Using Sub-character Level Information
Longtu Zhang | Mamoru Komachi
Proceedings of the Third Conference on Machine Translation: Research Papers

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.

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TMU Japanese-Chinese Unsupervised NMT System for WAT 2018 Translation Task
Longtu Zhang | Yuting Zhao | Mamoru Komachi
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation