Leveraging Text-side Augmentation For Sign Language Translation

Sign language translation faces significant challenges due to the scarcity of annotated data and the inherent complexity of sign languages. This paper presents a method to improve sign-to-text translation models by augmenting data on the text side. We conduct experiments using two state-of-the-art models on two publicly available datasets: PHOENIX-2014T for German Sign Language and Mediapi-RGB for French Sign Language. Our main contributions are : (1) augmenting the training sets of both datasets on the text side using a generative model, (2) evaluating the impact of paraphrasing on BLEU and BLEURT scores, and (3) analyzing the impact of paraphrasing on translation outputs. We observed a significant improvement in translation for both languages. This suggests that adding variability to the training dataset through paraphrasing can lead to better generalization of the models. These results are comparable to state-of-the-art methods that use more complex approaches, such as Visual-Language fine-tuning, to improve translation.