Neural Machine Translation Modelwith a Large Vocabulary Selected by Branching Entropy
Table 2: Automatic evaluation results (BLEU)
['System', 'ja → ch', 'ch → ja', 'ja → en', 'en → ja']
When compared with the baseline SMT, the performance gains of the proposed system are approximately 5.2 BLEU points when translating Japanese into Chinese and 7.1 BLEU when translating Chinese into Japanese. When compared with the baseline SMT, the performance gains of the proposed system are approximately 10.0 BLEU points when translating Japanese into English and 10.8 BLEU when translating English into Japanese. When compared with the result of the baseline NMT, the proposed NMT model achieved performance gains of 1.2 BLEU points on the task of translating Japanese into Chinese and 1.7 BLEU points on the task of translating Chinese into Japanese. When compared with the result of the baseline NMT, the proposed NMT model achieved performance gains of 0.4 BLEU points on the task of translating Japanese into English and 1.4 BLEU points on the task of translating English into Japanese. Furthermore, we quantitatively compared our study with the work of Luong et al. The proposed NMT model achieves performance gains of 0.8 BLEU points when translating Japanese into Chinese, and performance gains of 1.3 BLEU points when translating Chinese into Japanese. The proposed NMT model achieves performance gains of 0.2 BLEU points when translating Japanese into English, and performance gains of 1.0 BLEU points when translating English into Japanese.