Microsoft Translator at WMT 2019:Towards Large-Scale Document-Level Neural Machine Translation
Table 2: SacreBLEU results for various ensembles of 12-layer sentence-level systems on new dev set (concatenated test2016, test2017, test2018) split by source language and combined. Ensembles are weighted equally when no weights are shown. (a) refers to a single model trained on filtered parallel data only, (c) refers to a models trained with back-translated data, fine-tuned on filtered parallel data.
['[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']
We experiment with (a) models that have been trained on filtered parallel data only and (c) models that have been trained with back-translated data and then fine-tuned on parallel filtered data. All models are 12-layer models, have been trained with the same training procedure and only differ in data and random initialization. We did not explore adding (b) models that were trained with back-translated data but without fine-tuning. After submission we found that small gains could be achieved by adding these to the mix as well. Unless stated differently, all models are weighted equally.