A Label Proportions Estimation Technique for Adversarial Domain Adaptation in Text Classification
Table 1: Results of Yelp Experiments.
['[BOLD] Task P->Q', '[BOLD] Accuracy SVM', '[BOLD] Accuracy DNN', '[BOLD] Accuracy DANN', '[BOLD] Accuracy DAN- LPE', '[BOLD] Estimation Results |^ [ITALIC] β−^ [ITALIC] γdl|', '[BOLD] Estimation Results |^ [ITALIC] β−^ [ITALIC] γb|', '[BOLD] Estimation Results |^ [ITALIC] β−^ [ITALIC] α|']
We found the DAN-LPE has a overall more accurate label proportions estimate than BBSE. In the first eight tasks ^α and ^β differ a lot and the DANN does not show much improvement over DNN. In some tasks it even degrades the classification performance. In these experiments, the DAN-LPE shows a significant gain because the label proportions estimate ^γ reduces the label shift. In the last four tasks the label proportions between the source and target domains are close and DANN gets the best accuracy in three of them. However, the DAN-LPE algorithm performs comparably with DANN in these tasks since is does not degrade in estimating β. It is worth mentioning that given accurate label shift estimates, we can also improve the classification accuracy by the prior probability adjustment [saerens2002adjusting], re-weighting the class importance in C. Here we focus on the behavior in the domain adapter and will not further discuss this aspect in this paper.