Low-Resource Text Classification using Domain-Adversarial Learning
Table 3: Results of the multi-domain experiments. The |D| column specifies the number of domains that the domain-classifier Q predicts. In the case where |D|=2, Q only learned to differentiate between the source and target domain. S DANNn is the accuracy of P on the source dataset when trained on n=0, resp. n=500 training elements from the target domain.
['F', '| [ITALIC] D|', 'S DANN500', 'S DANN0', 'T DANN500', 'T DANN0']
As one can see, treating the domains separately (|D|=5) improves the performance on the source and target domains considerably. This suggests that the general DANN architecture is capable of learning indifferent feature spaces for multiple (|D|>2) domains.