Signed Language Processing (SLP) concerns the automated processing of signed
languages, the main means of communication of Deaf and hearing impaired
individuals. SLP features many different tasks, ranging from sign recognition
to translation and production of signed speech, but has been overlooked by the
NLP community thus far. In this paper, we bring to attention the task of
modelling the phonology of sign languages. We leverage existing resources to
construct a large-scale dataset of American Sign Language signs annotated with
six different phonological properties. We then conduct an extensive empirical
study to investigate whether data-driven end-to-end and feature-based
approaches can be optimised to automatically recognise these properties. We
find that, despite the inherent challenges of the task, graph-based neural
networks that operate over skeleton features extracted from raw videos are able
to succeed at the task to a varying degree. Most importantly, we show that this
performance pertains even on signs unobserved during training.

本研究介绍了自然语言处理（NLP）领域中较为被忽视的手语处理任务，其中重点关注手语音系学建模的任务。通过构建一个大规模的美国手语数据集并运用 数据驱动的端到端和基于特征的方法，研究表明使用基于骨骼特征提取的基于图的神经网络模型可以在不同程度上自动识别六种不同的手语音系学特性， 哪怕在没有经过训练的手语中也可以进行这种自动识别。