Abstract
We propose a new neural transfer method
termed Dual Adversarial Transfer Network
(DATNet) for addressing low-resource Named
Entity Recognition (NER). Specifically, two
variants of DATNet, i.e., DATNet-F and
DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator
(GRAD). Additionally, adversarial training is
adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains
and languages, and show that significant improvement can be obtained especially for lowresource data, without augmenting any additional hand-crafted features and pre-trained
language model.