Overview of Sign Language Translation Based on Natural Language Processing - Wang Hanmo (ITM Web of Conferences)
This paper explores the progress, challenges, and future directions in Sign Language Translation (SLT) within the broader field of Sign Language Processing (SLP), which combines Computer Vision (CV) and Natural Language Processing (NLP) to translate sign language videos into spoken language texts. The study begins by examining various sign language representation methods, such as video, gesture, symbol systems, and annotation, analyzing their strengths and weaknesses. It highlights the critical need for high-quality, large-scale datasets to advance SLT research, while acknowledging challenges like data scarcity, annotation inconsistencies, and ethical concerns. The paper then reviews recent SLT research, identifying key challenges and proposing solutions, such as expanding datasets through collaboration with the deaf and hard-of-hearing community, and employing advanced data collection techniques. Additionally, it suggests applying NLP methods like transfer learning and large language models to address specific challenges. Finally, the paper advocates for stronger interdisciplinary collaboration between CV and NLP to develop models and algorithms that are better suited to the unique aspects of sign languages.