A Survey on Neural Machine Translation Applied to Sign Language Generation

• Published in: 2021 3rd International Conference on Applied Machine Learning (ICAML) pp. 413 - 417
• Main Authors: Zhang, Yue, Cao, Lihong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2021
• Subjects: Auditory system; Data preprocessing; Gesture recognition; Machine learning; Neural Machine Translation; Performance evaluation; Pipelines; Recurrent neural networks; Sign Language; Sign Language Generation; Sign Language Processing; Sign Language Translation
• DOI: 10.1109/ICAML54311.2021.00093

Sign languages are the primary medium of communication for the deaf and hearing-impaired community, and the number of human beings with hearing impairment is increasing at a rapid rate. There is a significant communication barrier between the hearing-impaired and normal-hearing communities. One solution to this issue is to offload the effort of text understanding for the deaf and hearing-impaired community in the form of Sign Language Generation (SLG). In this paper, first of all, we systematically review the mainstream Neural Machine Translation (NMT)-based SLG approaches. Specifically, according to their model architectures, we divide them into two categories: the Recurrent Neural Networks (RNNs)-based and Transformer-based models, and discuss their advantages and limitations. Next, we introduce the existing publicly available datasets and the data preprossessing methods in the field of SLG. Then, we summarize the performance evaluation methods for NMT-based SLG models. Finally, we offer two possible future research directions for better development in the field of SLG.