Bridging the domain gap in cross-lingual document classification
Table 4: Error rates when using different augmentation strategies and their combinations. Results for sentiment classification shown on the left, and news document classification on the right.
['train test', 'amazon-fr', 'amazon-en amazon-de', 'amazon-cn', 'MLDoc en fr', 'MLDoc en de', 'MLDoc en es', 'MLDoc en zh', 'MLDoc en ru']
From the results, we conclude that t2t is the best performing approach, as it’s the best matched to the target domain. Leveraging the unlabeled data from other domains does not offer consistent improvement, however can provide additional value in isolated cases.