Low-Resource Text Classification using Domain-Adversarial Learning
Table 2: Classifier accuracies on the 3-class classification tasks. DANNn is the domain adversarial model trained on n labeled examples from the respective target domain. The S only column shows the accuracy of the models trained with source data without adversarial training, while for S+T the model is trained on source domain data and 500 target domain data points
['Features', 'DANN500', 'DANN0', 'S only', 'S+T']
The models trained only on documents of the source domain perform poorly on the target data. Moreover, the more complex Fcnn and Fhan model architectures perform worse in this scenario than the simpler Favg and Ftfidf models. This indicates, that the complex models overfit on the source domain. In comparison, the models using the adversarial training of Q, are regularized and do not overfit on the source domain, even if no labeled target data is used.