Bridging the domain gap in cross-lingual document classification
Table 5: The first part is the baseline results using the English unlabeled data. The second part is the results using the unlabeled data from the target domain, which are copied from the section 4.3.
['lang of test', 'Unlabeled', 'amazon-fr', 'amazon-de', 'amazon-cn', 'fr', 'de', 'es', 'zh', 'ru']
Here, we provide a baseline only using English samples to perform semi-supervised learning. More specifically, we first train the model with English unlabeled data and augmented samples, then tests it on different target domains. This approach is similar to the traditional translate-test method. This method offers a baseline, which merely increases the size of data but without providing the target domain information. During the test phrasing, we experiment with two input strategies. One is using the original test samples, and another is translating the samples into English. First, we observe that the performance of using original and translated samples is similar.