Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report
Table 3: Results of noisy data generation. We reverse the source and target direction of MTNT Fr2En (En2Fr) dev-set to evaluate the ability of noisy data generation for En2Fr (Fr2En).
['[EMPTY]', 'En2Fr', 'Fr2En']
To make use of monolingual target data, we want to generate the corresponding parallel pseudo noisy source data and put them into training set. In this experiment, we mix the clean and noisy dataset as the training set, but use the target sentences in reversed direction of noisy dataset (training, validation, test) set as source and source sentences as target. The domain insensitive method simply mix the clean and noisy dataset in training while the domain sensitive method differentiate the clean and noisy dataset in target side by starting with different symbol (e.g.  ,  ). The experiment shows that the domain sensitive method can outperform the domain insensitive method with a large margin.