Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction
Yinfei Yang, Oshin Agarwal, Chris Tar, Byron C. Wallace, Ani Nenkova
Abstract
Modern NLP systems require high-quality annotated data. For specialized domains, expert annotations may be prohibitively expensive; the alternative is to rely on crowdsourcing to reduce costs at the risk of introducing noise. In this paper we demonstrate that directly modeling instance difficulty can be used to improve model performance and to route instances to appropriate annotators. Our difficulty prediction model combines two learned representations: a ‘universal’ encoder trained on out of domain data, and a task-specific encoder. Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing. Further, our experiments confirm the expectation that for such domain-specific tasks expert annotations are of much higher quality and preferable to obtain if practical and that augmenting small amounts of expert data with a larger set of lay annotations leads to further improvements in model performance.- Anthology ID:
- N19-1150
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1471–1480
- Language:
- URL:
- https://aclanthology.org/N19-1150
- DOI:
- 10.18653/v1/N19-1150
- Cite (ACL):
- Yinfei Yang, Oshin Agarwal, Chris Tar, Byron C. Wallace, and Ani Nenkova. 2019. Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1471–1480, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction (Yang et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/N19-1150.pdf
- Data
- EBM-NLP