Facebook AI’s WAT19 Myanmar-English Translation Task Submission
Table 3: BLEU scores of systems trained only on the provided parallel datasets.
['[EMPTY]', 'Description', 'My → En', 'En → My']
We submitted a machine translation system that only uses the provided ALT and UCSY parallel datasets, without any additional monolingual data, results are reported in Tab. The baseline system achieves 23.3 BLEU points for My→En and 34.9 for En→My . Ensembling 5 models yields +1.8 BLEU points gain for My→En and +1.0 point for En→My . To apply noisy channel reranking, we train language models using data from the ALT and UCSY training set. The language model architectures are the same for both languages, each has 5 transformer layers, 4 attention heads, 256 embedding dimensions and 512 inner-layer dimensions. Noisy channel ranking yields a gain of +1.2 BLEU points for My→En and +1.0 points for En→My on top of the ensemble models. If we consider submissions that only use the provided parallel data (see rows that are not highlighted), our submission improves upon the second best system by 7.2 BLEU in My→En and 10.9 BLEU in En→My . This suggests that our baseline system is very strong and that applying ST and BT to the parallel dataset is a good way to build even stronger baselines, as demonstrated also in Tab.