Facebook AI’s WAT19 Myanmar-English Translation Task Submission
Table 2: Combining BT and ST yields better BLEU score than BT and ST.
['Model', 'My→En', 'En→My']
Specifically, we combine bitext data together with self-trained and back-translated data, {X,Y}∪{←g(MT),MT}∪{(MS,→f(MS)}. As for BT, we upsample the bitext data, concatenate it with the forward and backward translations and train a new forward model from scratch. The upsample ratios for each dataset are tuned via hyper-parameter search on the validation set. Finally, the gains brought by monolingual datasets is striking only in My→En (+11.8 BLEU points in My→En compared to only +2.5 BLEU points in En→My , for our submissions). The reason is because the ALT test set originates from English news and the target English monolingual data is high quality and in-domain with the test set. Moreover, the source originating Myanmar sentences are translationese of English news sentences, a setting which is particularly favorable to BT. Instead, Myanmar monolingual data is out-of-domain and noisy which makes BT much less effective. ST helps improving BT performance as shown in Tab. the gains are still limited.