A Label Proportions Estimation Technique for Adversarial Domain Adaptation in Text Classification
Table 2: Results of Behavioral Coding Experiments.
['[BOLD] Task P->Q', '[BOLD] F-score DNN', '[BOLD] F-score DANN', '[BOLD] F-score DAN-LPE', '[BOLD] Estimation Results |^ [ITALIC] β−^ [ITALIC] γdl|', '[BOLD] Estimation Results |^ [ITALIC] β−^ [ITALIC] γb|', '[BOLD] Estimation Results |^ [ITALIC] β−^ [ITALIC] α|']
It gains the highest F-score in most tasks except in the last one when the estimated proportions do not decrease the label shift much. The DANN only has a comparable F-score compared with DNN and even degrades for some tasks. The DNN results suggest that we find the behavioral coding task is harder than the yelp experiment because the number of classes is larger and the behavior codes are human defined and not uncorrelated. However, DAN-LPE shows its robustness and still gives reasonable proportion estimate of the data in unlabelled domain.