Machine Translation of Low-Resource Spoken Dialects:Strategies for Normalizing Swiss German
Table 4: BLEU scores for various test sets (Bern, Zurich, Valais dialects) for a Moses-based system trained over data including the two GSW/DE dictionaries with two language models (LM).
['Test set', 'BLEU Small LM', 'BLEU Large LM']
As expected, using more training data in the form of bilingual lexicons yields more reliable translation models. We observe that the scores decrease slightly for the Bernese test sets, and hypothesize that this is due to the different domains of the language model and the test set. However, as the larger language model is trained on more diverse data, we will keep using it below for its robustness.