Regressing Word and Sentence Embeddingsfor Regularization of Neural Machine Translation
TABLE I: BLEU scores over the En-Fr test set. The reported results are the average of 5 independent runs.
['[BOLD] Models', 'Word/BPE', 'BLEU']
We have carried out a number of experiments with both baselines. The scores reported are an average of the BLEU scores (in percentage points, or pp) In this case, the models with ReWE have outperformed the LSTM and transformer baselines consistently. The LSTM did not benefit from using BPE, but the transformer+ReWE with BPE reached 36.30 BLEU pp (a +0.99 pp improvement over the best model without ReWE). For this dataset we did not use ReSE because French was the target language.