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Computer Science > Computation and Language

arXiv:1809.02694 (cs)
[Submitted on 7 Sep 2018]

Title:Neural Machine Translation of Logographic Languages Using Sub-character Level Information

Authors:Longtu Zhang, Mamoru Komachi
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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.
Comments: WMT 2018 (regular paper); 9 pages
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:1809.02694 [cs.CL]
  (or arXiv:1809.02694v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.1809.02694

Submission history

From: Mamoru Komachi [view email]
[v1] Fri, 7 Sep 2018 22:02:43 UTC (328 KB)
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