Lessons Learned from a Citizen Science Project for Natural Language Processing

This is the accompanying data for our paper "Lessons Learned from a Citizen Science Project for Natural Language Processing".

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.

Identifier
Source https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3942
Related Identifier https://aclanthology.org/2023.eacl-main.261/
Metadata Access https://tudatalib.ulb.tu-darmstadt.de/oai/openairedata?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:tudatalib.ulb.tu-darmstadt.de:tudatalib/3942
Provenance
Creator Klie, Jan-Christoph; Lee, Ji-Ung; Stowe, Kevin; Sahin, Gözde Gül; Moosavi, Nafise Sadat; Bates, Luke; Dominic, Petrak; Eckart de Castilho, Richard; Gurevych, Iryna
Publisher TU Darmstadt
Contributor TU Darmstadt
Publication Year 2023
Rights Creative Commons Attribution-NonCommercial 4.0; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://tudatalib.ulb.tu-darmstadt.de/page/contact
Representation
Resource Type Dataset
Format application/zip
Discipline Other