Study on pose-based deep learning models for gloss-free Sign Language Translation

Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, different grammars and lack of data. Currently, many SLT models rely on intermediate gloss annotations as outputs or latent priors. Glosses can help models to correctly segment and align signs to better understand the video. How- ever, the use of glosses comes with significant limitations, since obtaining annotations is quite difficult. Therefore, scaling gloss-based models to millions of samples remains impractical, specially considering the scarcity of sign language datasets. In a similar fashion, many models use video data that requires larger models which typically only work on high end GPUs, and are less invariant to signers appearance and context. In this work we propose a gloss-free pose-based SLT model. Using the extracted pose as feature allow fora sign significant reduction in the dimensionality of the data and the size of the model. We evaluate the state of the art, compare available models and develop a keypoint-based Transformer model for gloss-free. SLT, trained on RWTH-Phoenix, a standard dataset for benchmarking SLT models alongside GSL, a simpler laboratory-made Greek Sign Language dataset.