ReWE: Regressing Word Embeddingsfor Regularization of Neural Machine Translation Systems
Table 7: Results of the Contrastive A experiment (λ=0.2; average of 10 models trained independently from different random seeds).
['[BOLD] Dataset', '[BOLD] BLEU Word', '[BOLD] BLEU BPE']
For this experiment, the value of λ has been specifically tuned over the er-fr validation set (highest score for λ=0.2). However, this configuration has rarely improved over our baseline (e.g., on the eu-en dataset), and it has performed considerably worse with the en-fr pair. This shows that, in comparison, the proposed joint learning is a much more effective setting.