Towards community-centered morphological annotation
I’ll be discussing joint work with Sara Court, Maria Copot, Noah Diewald and Stephanie Antetomaso, covering work from our recent ComputeEL publication and slightly updated version for Language Documentation & Archiving.
I hope to discuss both the existing work (for which an abstract is below) and also some of the upcoming challenges as we attempt to develop the learning part of the process into a usable and deployable part of the user experience.
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.