Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling
Table 6: We retrain the monolingual systems DE, FR using the original training sets (BL: Original) shown in Table 1 and inject our generated data in different sizes. We also compare to the stronger baseline LabelProj where we add data created by label projection Akbik et al. (2015).
['Model + Training Data', 'Data Size', 'F1 Test']
Data Augmentation Finally, we augment the training sets of our two resource-poor languages DE and FR, in portions of 10K until we cover the complete generated data. We compare the increase in F1 score when training models with different amounts of additional data. We also add a comparison of the improvement achieved when adding the same amount of sentences produced by the labeled projection method of Akbik et al. Presumably we see this improvement because the frequency of the major roles is more prominent. In the case of French, we don’t see significant improvement, however also here the addition of projected data shows a similar trend.