Multinomial Adversarial Networks for Multi-Domain Text Classification
Table 1: MDTC results on the Amazon dataset. Models in bold are ours while the performance of the rest is taken from wu2015. Numbers in parentheses indicate standard errors, calculated based on 5 runs. Bold numbers indicate the highest performance in each domain, and ∗ shows statistical significance (p<0.05) over CMSC under a one-sample T-Test.
['[EMPTY]', 'Book', 'DVD', 'Elec.', 'Kit.', 'Avg.']
Within each category, various architectures are examined, such as Least Square (LS), SVM, and Logistic Regression (LR). As explained before, we use MLP as our feature extractors for all our models (bold ones). Among our models, the ones with the MAN prefix use adversarial training, and MAN-L2 and MAN-NLL indicate the L2 loss and NLL loss MAN, respectively. This is corroborated by the fact that our SP-MLP model performs comparably to CMSC, while the latter relies on external resources such as sentiment lexica. Moreover, when our multinomial adversarial nets are introduced, further improvement is observed. With both loss functions, MAN outperforms all Shared-Private baseline systems on each domain, and achieves statistically significantly higher overall performance. For our MAN-SP models, we provide the mean accuracy as well as the standard errors over five runs, to illustrate the performance variance and conduct significance test. It can be seen that MAN’s performance is relatively stable, and consistently outperforms CMSC.