Regressing Word and Sentence Embeddingsfor Regularization of Neural Machine Translation
TABLE II: BLEU scores over the Cs-En test set. The reported results are the average of 5 independent runs.
['[BOLD] Models', 'Word/BPE', 'BLEU']
Also in this case, all the models with ReWE have improved over the corresponding baselines. The LSTM+ReWE has achieved the best results (23.72 BLEU pp; an improvement of +1.16 pp over the best model without ReWE). This language pair has also benefited more from the BPE pre-processing, likely because Czech is a morphologically-rich language. For this dataset, it was possible to use ReSE in combination with ReWE, with an improvement for the LSTM at word level (+0.14 BLEU pp), but not for the remaining cases. We had also initially tried to use ReSE without ReWE (i.e., λ=0), but the results were not encouraging and we did not continue with this line of experiments.