AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging
Bill Yuchen Lin, Dong-Ho Lee, Frank F. Xu, Ouyu Lan, Xiang Ren
Abstract
We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER). The distinctive advantages of AlpacaTag are three-fold. 1) Active intelligent recommendation: dynamically suggesting annotations and sampling the most informative unlabeled instances with a back-end active learned model; 2) Automatic crowd consolidation: enhancing real-time inter-annotator agreement by merging inconsistent labels from multiple annotators; 3) Real-time model deployment: users can deploy their models in downstream systems while new annotations are being made. AlpacaTag is a comprehensive solution for sequence labeling tasks, ranging from rapid tagging with recommendations powered by active learning and auto-consolidation of crowd annotations to real-time model deployment.- Anthology ID:
- P19-3010
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Marta R. Costa-jussà, Enrique Alfonseca
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 58–63
- Language:
- URL:
- https://aclanthology.org/P19-3010
- DOI:
- 10.18653/v1/P19-3010
- Cite (ACL):
- Bill Yuchen Lin, Dong-Ho Lee, Frank F. Xu, Ouyu Lan, and Xiang Ren. 2019. AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 58–63, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging (Lin et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P19-3010.pdf