A GRU-Gated Attention Model for Neural Machine Translation© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Table 2: BLEU and TER scores of the ensemble of different NMT systems.
['[BOLD] Metric', '[BOLD] System', '[BOLD] MT05', '[BOLD] MT02', '[BOLD] MT03', '[BOLD] MT04', '[BOLD] MT06', '[BOLD] MT08', '[BOLD] AVG']
We further testify whether the ensemble of our models and RNNSearch can generate better performance against any single system. We ensemble different systems by simply averaging their predicted target word probabilities at each decoding step, as suggested in [luong-EtAl:2015:ACL-IJCNLP]. Not surprisingly, all the ensemble systems achieves significant improvements over the best single system. And the ensemble of “RNNSearch+GAtt+GAtt-Inv” produces the best results, 38.64 BLEU and 54.15 TER scores on average. This demonstrates that these neural models are complementary and beneficial to each other.