ReWE: Regressing Word Embeddingsfor Regularization of Neural Machine Translation Systems
Table 2: BLEU scores over the test sets. Average of 10 models independently trained with different seeds.
['[BOLD] Models', '[BOLD] en-fr Word', '[BOLD] en-fr BPE', '[BOLD] cs-en Word', '[BOLD] cs-en BPE', '[BOLD] eu-en Word', '[BOLD] eu-en BPE']
The values of our experiments are for blind runs over the test sets, averaged over 10 independent runs with different seeds. The results show that adding ReWE has significantly improved the baseline in all cases, with an average of 1.46 BLEU points. In the case of the eu-en dataset, the improvement has reached 2.54 BLEU points. We have also run unpaired t-tests between our baseline and ReWE, and the differences have proved statistically significant (p-values <0.05) in all cases. Using BPE has proved beneficial for the cs-en and eu-en pairs, but not for the en-fr pair. We speculate that English and French may be closer to each other at word level and, therefore, less likely to benefit from the use of sub-word units. Conversely, Czech and Basque are morphologically very rich, justifying the improvements with BPE. This shows that the proposed model is competitive with contemporary NMT techniques.