The Source-Target Domain Mismatch Problem in Machine Translation
Table 3: BLEU scores for the Nepali to English and English to Myanmar translation task.
['Model', 'Ne → En', 'En → My']
or back-translated monolingual data achieves almost 2 BLEU points improvement over the supervised baseline. On the En-My task BT slightly outperforms the baseline, while ST improves by +2.5 BLEU, since source side monolingual data is in-domain with the test set, while target side monolingual data is scarce and out-of-domain. On both tasks, we observe that combining ST and BT outperforms each individual method.