Adapting Word Representations Across Corpora
Table 8: Micro and Macro accuracies over all classes and Macro accuracy for the 50% rarest classes.
['Method', 'Micro', 'Macro', 'Rare classes']
We also show the macro accuracy of the 50% rarest classes. We find that our snippet selection method WECT provides the highest gains over the baseline. Further, regularizing using our word stability measure provides more useful embeddings than Yang17’s stability scores. Also, while Src-Tune performs better than regularization-based method, it is still inferior to WECT on all three metrics. The X-axis lists the classes in increasing order of frequency. We find that our method achieves impressive gains in the left side corresponding to low frequency classes. In the first class, Tgt alone gave 50% accuracy, whereas WECT gave 100%. For the second class, Tgt alone got 0% accuracy whereas WECT got 25%.