The Sensitivity of Annotator Bias to Task Definitions in Argument Mining

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NLP models are dependent on the data they are trained on, including how this data is annotated. NLP research increasingly examines the social biases of models, but often in the light of their training data and specific social biases that can be identified in the text itself. In this paper, we present an annotation experiment that is the first to examine the extent to which social bias is sensitive to how data is annotated. We do so by collecting annotations of arguments in the same documents following four different guidelines and from four different demographic annotator backgrounds. We show that annotations exhibit widely different levels of group disparity depending on which guidelines annotators follow. The differences are not explained by task complexity, but rather by characteristics of these demographic groups, as previously identified by sociological studies. We release a dataset that is small in the number of instances but large in the number of annotations with demographic information, and our results encourage an increased awareness of annotator bias.

Original languageEnglish
Title of host publicationProceedings of the 16th Linguistic Annotation Workshop, LAW 2022 - held in conjunction with the Language Resources and Evaluation Conference, LREC 2022 Workshop
EditorsSameer Pradhan, Sandra Kubler
PublisherEuropean Language Resources Association (ELRA)
Publication date2022
ISBN (Electronic)9782493814081
Publication statusPublished - 2022
Event16th Linguistic Annotation Workshop, LAW 2022 - Marseille, France
Duration: 24 Jun 2022 → …


Conference16th Linguistic Annotation Workshop, LAW 2022
Periode24/06/2022 → …

Bibliographical note

Funding Information:
Many thanks to Anna Rogers and Carsten Eriksen for their insightful comments. Maria Barrett is supported by a research grant (34437) from VILLUM FONDEN.

Publisher Copyright:
© 2022 European Language Resources Association (ELRA).

    Research areas

  • Annotation, argument mining, bias

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