Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

Paul Rottger, Bertie Vidgen, Dirk Hovy, Janet Pierrehumbert


Abstract
Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged annotator subjectivity, but rarely actively managed it in the annotation process. This has led to partly-subjective datasets that fail to serve a clear downstream use. To address this issue, we propose two contrasting paradigms for data annotation. The descriptive paradigm encourages annotator subjectivity, whereas the prescriptive paradigm discourages it. Descriptive annotation allows for the surveying and modelling of different beliefs, whereas prescriptive annotation enables the training of models that consistently apply one belief. We discuss benefits and challenges in implementing both paradigms, and argue that dataset creators should explicitly aim for one or the other to facilitate the intended use of their dataset. Lastly, we conduct an annotation experiment using hate speech data that illustrates the contrast between the two paradigms.
Anthology ID:
2022.naacl-main.13
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
175–190
Language:
URL:
https://aclanthology.org/2022.naacl-main.13
DOI:
10.18653/v1/2022.naacl-main.13
Bibkey:
Cite (ACL):
Paul Rottger, Bertie Vidgen, Dirk Hovy, and Janet Pierrehumbert. 2022. Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 175–190, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks (Rottger et al., NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.13.pdf
Video:
 https://aclanthology.org/2022.naacl-main.13.mp4
Code
 paul-rottger/annotation-paradigms