Bootstrapping Sign Language Annotations with Sign Language Models

Apple Machine Learning Journal / 4/30/2026

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Key Points

The paper addresses the bottleneck that AI-based sign language interpretation is limited by insufficient high-quality annotated datasets.
It highlights new datasets such as ASL STEM Wiki and FLEURS-ASL that include professional interpreters and many hours of footage, but are only partially annotated and therefore not fully exploitable.
To reduce annotation cost, the authors propose a pseudo-annotation pipeline that ingests signed video plus English text and produces a ranked set of likely annotations with time intervals.
The pipeline targets multiple annotation types, including glosses, fingerspelled words, and sign classifier outputs, leveraging sparse predictions to bootstrap further labeling.

AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters and 100s of hours of data but remain only partially annotated and thus underutilized, in part due to the prohibitive costs of annotating at this scale. In this work, we develop a pseudo-annotation pipeline that takes signed video and English as input and outputs a ranked set of likely annotations, including time intervals, for glosses, fingerspelled words, and sign classifiers. Our pipeline uses sparse predictions from…

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