Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition
Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, Kenneth Kwok
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.- Anthology ID:
- P19-1336
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3461–3471
- Language:
- URL:
- https://aclanthology.org/P19-1336
- DOI:
- 10.18653/v1/P19-1336
- Cite (ACL):
- Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, and Kenneth Kwok. 2019. Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3461–3471, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition (Zhou et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P19-1336.pdf
- Data
- CoNLL 2002, CoNLL 2003