Novel Applications of Factored Neural Machine Translation
Table 1: Automatic evaluation results (in %).
['[BOLD] English→German', 'Bleu  [BOLD] newstest 2017', 'Ter  [BOLD] newstest 2017', 'Bleu  [BOLD] newstest 2019', 'Ter  [BOLD] newstest 2019']
For En→De newstest2019, both a second factor predicting word casing as well as a second factor predicting subword merges resulted in identical Bleu scores to the baseline and only in a minor degradation in terms of Ter. On En→De newstest2017, a tendency towards improvement can be observed for these factored systems. The slightly worse performance of casing prediction may indicate that it is most effective for German, where capitalized words are much more frequent. We found casing prediction via a factor to work indistinguishably well as compared to using truecased subwords. Case-insensitive Bleu and Ter scores for the baseline system and the one with the casing factor were found to be almost identical, e.g. 37.9% \textscBleu on En→De newstest2019 for both systems. \textscBleu, respectively). this indicates that the systems neither differ significantly in the choice of words nor in their casing.