Abstract
Crowdsourcing offers a convenient means of obtaining labeled data quickly and inexpensively. However, crowdsourced labels are often noisier than expert-annotated data, making it difficult to aggregate them meaningfully. We present an aggregation approach that learns a regression model from crowdsourced annotations to predict aggregated labels for instances that have no expert adjudications. The predicted labels achieve a correlation of 0.594 with expert labels on our data, outperforming the best alternative aggregation method by 11.9%. Our approach also outperforms the alternatives on third-party datasets.- Anthology ID:
- D17-1204
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1907–1912
- Language:
- URL:
- https://aclanthology.org/D17-1204
- DOI:
- 10.18653/v1/D17-1204
- Cite (ACL):
- Natalie Parde and Rodney Nielsen. 2017. Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1907–1912, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data (Parde & Nielsen, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/D17-1204.pdf