Refining Source Representations with Relation Networks for Neural Machine Translation
Table 1: Performance comparison on NIST datasets. ∗ is used to indicate the improvement over RNNsearch⋆ is statistically significant [Collins et al.2005] (p<0.01).
['[BOLD] Systems', '[BOLD] MT03', '[BOLD] MT04', '[BOLD] MT05', '[BOLD] MT06', '[BOLD] Average']
We compared our system Rnmt with the two baseline systems RNNsearch and RNNsearch⋆ both on the NIST Zh-En and the WMT17 En-De translation tasks. As Rnmt was implemented on the basis of RNNsearch⋆, in the strict sense, RNNsearch⋆ is the baseline. Besides, comparison between our model to VRNMT shows that proposed simple model stably produces better performance on all test datasets and outperforms VRNMT 1.04 BLEU score on average.