Jan-Christoph, K., Lee, J.-U., Stowe, K. et al. (6 more authors) (2023) Lessons learned from a Citizen Science project for Natural Language Processing. In: Vlachos, A. and Augenstein, I., (eds.) Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 17th Conference of the European Chapter of the Association for Computational Linguistics, 02-06 May 2023, Dubrovnik, Croatia. Association for Computational Linguistics , pp. 3594-3608. ISBN 9781959429449
Abstract
Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and at- tract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2023 Association for Computational Linguistics (ACL). This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 May 2023 09:29 |
Last Modified: | 08 Dec 2023 16:40 |
Published Version: | https://aclanthology.org/2023.eacl-main.261 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199093 |