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GNMT improves on the quality of translation by applying an example based (EBMT) machine translation method in which the system "learns from millions of examples".[2] GNMT's proposed architecture of system learning was first tested on over a hundred langua

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GNMTは、システムが「数百万の例から学ぶ」という例に基づいた（ EBMT ）機械翻訳方法を適用することで、翻訳の品質を向上させます。[2] GNMTが提案したシステム学習のアーキテクチャは、最初に100を超えてテストされました。

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GNMT improves the quality of translation by applying (EBMT) machine translation methods based on the example that the system “learns from millions of examples.” [2] The architecture of system learning proposed by GNMT was initially tested over 100 times.

INTO JAPANESE

GNMTは、システムが「数百万の例から学ぶ」という事例に基づいて、機械翻訳法（ EBMT ）を適用することで翻訳の品質を向上させます。[2] GNMTが提案したシステム学習のアーキテクチャは、最初に100回以上テストされました。

BACK INTO ENGLISH

GNMT improves the quality of translation by applying machine translation methods (EBMT) based on the case that the system “learns from millions of examples.” [2] The architecture of system learning proposed by GNMT was first tested more than 100 times.

INTO JAPANESE

GNMTは、システムが「数百万の例から学ぶ」という事例に基づいて、機械翻訳方法（ EBMT ）を適用することで翻訳の質を向上させます。[2] GNMTが提案したシステム学習のアーキテクチャは、最初に100回以上テストされました。

BACK INTO ENGLISH

GNMT improves the quality of translation by applying machine translation methods (EBMT) based on the case that the system “learns from millions of examples.” [2] The architecture of system learning proposed by GNMT was first tested more than 100 times.