Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling
Canasai Kruengkrai, Thien Hai Nguyen, Sharifah Mahani Aljunied, Lidong Bing
Abstract
Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate. Current models for jointly learning sentence and token labeling are limited to binary classification. We present a joint model that supports multi-class classification and introduce a simple variant of self-attention that allows the model to learn scaling factors. Our model produces 3.78%, 4.20%, 2.08% improvements in F1 over the BiLSTM-CRF baseline on e-commerce product titles in three different low-resource languages: Vietnamese, Thai, and Indonesian, respectively.- Anthology ID:
- 2020.acl-main.523
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5898–5905
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.523
- DOI:
- 10.18653/v1/2020.acl-main.523
- Cite (ACL):
- Canasai Kruengkrai, Thien Hai Nguyen, Sharifah Mahani Aljunied, and Lidong Bing. 2020. Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5898–5905, Online. Association for Computational Linguistics.
- Cite (Informal):
- Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (Kruengkrai et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/landing_page/2020.acl-main.523.pdf