Dual Past and Future for Neural Machine Translation
Table 1: Case-insensitive BLEU scores (%) on the NIST Chinese-to-English (ZH-EN) task. The improvements over the Transformer baseline Vaswani et al. (2017) are in the superscript.
['[BOLD] System', '[BOLD] Speed', '[BOLD] MT06', '[BOLD] MT02', '[BOLD] MT03', '[BOLD] MT04', '[BOLD] MT05', '[BOLD] Average', 'Δ']
We mainly conduct our experiments on the NIST Chinese-to-English (ZH-EN) translation task. Besides our implemented NMT systems, we also list the performance of several existing systems Wang et al. Compared with previous adequacy-oriented method Zheng et al. Although our model introduces approximately once more numbers of the parameters in Transformer-base, it does not hurt the decoding speed too much (0.87×) because we use dual models for training but only one side model is used for testing. Also, we report the performance of EN-ZH translation produced by our model. Compared with Transformer baseline, our method improves the translation by +0.66 BLEU scores.