Application of Language Learning Methodologies in Portuguese Sign Language Translation

Sign language (SL) translation aims to facilitate communication between deaf and hearing individuals. The complex, and yet important, task has encouraged the development of innovative tools, but the literature lacks in examples of solutions that represent the naturalness, continuity and multimodality required for sign language sentences. This paper explores an innovative approach to the development of a continuous sign language recognition (CSLR) and translation system, by applying language learning principles. The proposed system is divided into two parts: the first explores a new approach, based on children's language learning process, to the training of a deep learning (DL) model, while the second focuses on the development of a multimodal DL architecture capable of recognising and translating continuous SL, using the implemented approach. The paper enphasis is put on the first part of the system, highlighting the key design choices aimed at mimicking human language acquisition in the model training process, the main differences between this and traditional state-of-the-art (SOTA) methodologies, and the advantages compared to other continuous sign language translation (CSLT) solutions. Although this work will be directed at Portuguese Sign Language, Lingua Gestual Portuguesa (LGP) translation, if successful, the methodology could be extended to other sign language translation systems or even broader natural language processing (NLP) tasks, contributing to a more inclusive and accessible communication framework.