A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers
Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig, Jaime Carbonell
Abstract
Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages. However, there are now many proposed solutions to this problem involving either cross-lingual transfer learning, which learns from other highly resourced languages, or active learning, which efficiently selects effective training data based on model predictions. In this paper, we ask the question: given this recent progress, and some amount of human annotation, what is the most effective method for efficiently creating high-quality entity recognizers in under-resourced languages? Based on extensive experimentation using both simulated and real human annotation, we settle on a recipe of starting with a cross-lingual transferred model, then performing targeted annotation of only uncertain entity spans in the target language, minimizing annotator effort. Results demonstrate that cross-lingual transfer is a powerful tool when very little data can be annotated, but an entity-targeted annotation strategy can achieve competitive accuracy quickly, with just one-tenth of training data.- Anthology ID:
- D19-1520
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5164–5174
- Language:
- URL:
- https://aclanthology.org/D19-1520
- DOI:
- 10.18653/v1/D19-1520
- Cite (ACL):
- Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig, and Jaime Carbonell. 2019. A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5164–5174, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (Chaudhary et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/landing_page/D19-1520.pdf
- Code
- Aditi138/EntityTargetedActiveLearning