Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data

Natalie Parde, Rodney Nielsen


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://www.aclweb.org/anthology/D17-1204
DOI:
10.18653/v1/D17-1204
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1204.pdf