Multinomial Adversarial Networks for Multi-Domain Text Classification
Table 2: Results on unlabeled domains. Models in bold are our models while the rest is taken from DBLP:journals/corr/ZhaoZWCMG17. Highest domain performance is shown in bold.
['Target Domain', 'Book', 'DVD', 'Elec.', 'Kit.', 'Avg.']
It shows that MAN outperforms several baseline systems, such as a MLP trained on the source-domains, as well as single-source domain adaptation methods such as mSDA Chen et al. et al. Finally, when compared to MDAN, MAN and MDAN each achieves higher accuracy on two out of the four target domains, and the average accuracy of MAN is similar to MDAN. Therefore, MAN achieves competitive performance for the domains without annotated corpus. Nevertheless, unlike MS-DA methods, MAN can handle multiple target domains at one time.