Bridging the domain gap in cross-lingual document classification
Table 6: Ablation Study about the translate-train strategies. 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']
As discussed earlier, fine-tuning XLM on the target language would depreciate the multilingual ability of the model. We apply the translate-train method to tackle this problem. In order to understand the influence of this strategy when using the proposed framework, we perform an ablation study. We test 3 input strategies: (1) English: use the original English data as training data. (2) tr-train: use the translate-train strategy, which translate the training data into the target language. (3) both: we combine the (1) and (2) as the training data.