Sign Language Translation based on Transformers for How2Sign | Patricia Cabot Álvarez | UPC 2022

Advisors: Laia Tarrés & Xavier Giró-i-Nieto The end goal of Sign Language Translation is to either produce spoken sentences from sign videos or generate sign videos from their corresponding written transcriptions. In this situation, this task has been address in multiple approaches in recent years. Moreover, it has been proved that taking advantage of the sign gloss representations improves substantially the model’s performance in this task. Therefore, in this work we replicate the state-of-the-art Transformer-based approach on the task and evaluate it on the multimodal American Sign Language How2Sign dataset. Furthermore, we provide baseline recognition and translation results that represent an starting point to further research on the topic. In addition, we provide a new sentence-based alignment for the How2Sign videos, as their current alignment was with speech, which we have used to tackle the Sign Language Translation task properly.