Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations
Table 4: Ensemble performances, where the Hybrid model denotes SAWR + Tree-RNN + Tree-Linearization.
['System', 'MT03', 'MT04', 'MT05', 'MT06', 'Average/Δ']
First, we can see that ensemble is one effective technique to improve the translation performances. More importantly, the results show that the heterogeneous ensemble achieves averaged BLEU improvements by 43.10−41.24=1.86 points, better than the gains achieved by all three homo-approach ensembles, denoting that the three approaches could be mutually complementary in representing dependency syntax, and the resulting models of the three approaches are highly diverse.