Microsoft Translator at WMT 2019:Towards Large-Scale Document-Level Neural Machine Translation
Table 3: SacreBLEU results for document-level systems on new devset. Missing numbers marked as * were not computed during our experiments.
['[EMPTY]', 'Separated by origin en', 'Separated by origin de', 'Separated by origin both', 'Original test sets 2016', 'Original test sets 2017', 'Original test sets 2018']
The BLEU scores follow largely the results for the sentence-level systems, including improved scores for deeper models. Document-level models with capital letters (A), (B), (C) have been trained on similar data sets as sentence-level systems (a), (b), (c) respectively. Both (C) and (c) have undergone similar fine-tuning procedures. It is interesting to see that decoding very long sequences of up to 1000 tokens does not seem to degrade translation performance compared to sentence-level systems. This persists during fine-tuning, but it is generally unclear if this is an actual improvement. Based on our strategy of preferring improvements on the originally-English side, we use the multi-task trained models from now on.