Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report
Table 4: Results of different methods on test-set. †Michel and Neubig (2018)
['[EMPTY]', 'Methods', 'En-Fr', 'Fr-En']
Similar with the previous experiments, the domain insensitive methods simply mix all the clean, noisy training data. The performance has a little improvement in En-Fr by adding the monolingual data paired with the pseudo source data generated by the model trained in previous experiments. To differentiate the clean and noisy dataset, we assign different label at the start of them and the performance is thus boosted about 3 to 4 BLEU score. We further generate pseudo noisy source data from the monolingual target with the model using the domain sensitive method in previous experiment. By adding these noisy back-translation data, we achieve more than 2 BLEU improvement. Our final submission ensembles 5 models trained with the domain sensitive method and including the noisy back translation data.