English | EN 
Recent advances in Natural Language Processing (NLP) have revolutionized access to automated language understanding, largely thanks to zero-shot learning capabilities. These breakthroughs have quickly expanded into multimodal input processing—whether it’s images, sound, or video—positioning LLMs as promising universal models capable of building meaningful representations regardless of content modality or format.
However, while NLP and computer vision have progressed rapidly, other essential communication systems—particularly those used in non-verbal contexts—have lagged behind. Sign Language Translation (SLT), for instance, remains a challenge for modern machine learning approaches due to limited data availability and region-specific variations.
In recent years, researchers in the SLT community have developed innovative methods to address these challenges. Notably, LLM-based architectures are being used to bootstrap semantic and syntactic knowledge from spoken language, helping to close the gap that has long made SLT elusive. These new approaches leverage pretrained models across text, image, and video to align visual and linguistic data, establishing visuo-semantic representations that could power the sign language interpreters of the future.
Architectures like SignLLM1 and the more recent SpaMo2 have opened new avenues for SLT to benefit directly from advancements in NLP, potentially transforming sign language processing through cross-modal learning.