Synthetic and Natural Noise Both BreakNeural Machine Translation
Table 2: Average number of available edits per word in natural noise datasets and the corresponding token recall of those edits on the training and test splits.
['French Words', 'French Errors', 'French Train', 'French Test', 'German Words', 'German Errors', 'German Train', 'German Test', 'Czech Words', 'Czech Errors', 'Czech Train', 'Czech Test']
We insert these errors into the source-side of the parallel data by replacing every word in the corpus with an error if one exists in our dataset. When there is more than one possible replacement to choose we sample uniformly. Words for which there is no error are kept as is. Despite the small size of the German and Czech datasets, we are able to replace up to half of the words in the corpus with errors. Due to the small size of the German and Czech datasets these percentages decrease for longer words (>4 characters) to 25% and 32%, respectively.