Zhou, J. T., Zhang, H., Jin, D., Zhu, H., Fang, M., Goh, R. S. M., Kwok, K. (2019). Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/p19-1336
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 low resource data, without augmenting any additional hand-crafted features and pre-trained
language model.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Advanced Manufacturing and Engineering (AME) Programmatic Fund
Grant Reference no. : A18A2b0046, A1718g0048
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Singapore Government’s Research, Innovation and Enterprise 2020 Plan, Advanced Manufacturing and Engineering domain
Grant Reference no. : A1687b0033, A18A1b0045