A word-and-paradigm workflow for fieldwork annotation

TitreA word-and-paradigm workflow for fieldwork annotation
Publication TypeArticle dans des actes
Année de la conférence2022
AuthorsCopot, Maria, Sara Court, Noah Diewald, Stephanie Antetomaso, and Micha Elsner
Nom de la conférenceComputEL
VolumeProceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages
PublisherAssociation for Computational Linguistics
Conference LocationDublin, Ireland

There are many challenges in morphological fieldwork annotation: it heavily relies on segmentation and feature labeling (which have both practical and theoretical drawbacks), it’s time-intensive, and the annotator needs to be linguistically trained and may still annotate things inconsistently. We propose a workflow that relies on unsupervised and active learning grounded in Word-and-Paradigm morphology (WP). Machine learning has the potential to greatly accelerate the annotation process and allow a human annotator to focus on problematic cases, while the WP approach makes for an annotation system that is word-based and relational, removing the need to make decisions about feature labeling and segmentation early in the process and allowing speakers of the language of interest to participate more actively, since linguistic training is not necessary. We present a proof-of-concept for the first step of the workflow: in a realistic fieldwork setting, annotators can process hundreds of forms per hour.