This paper presents a series of experiments on translating between spoken Spanish and Spanish Sign Language glosses (LSE), including enriching Neural Machine Translation (NMT) systems with linguistic features, and creating synthetic data to pretrain and later on finetune a neural translation model. We found evidence that pretraining over a large corpus of LSE synthetic data aligned to Spanish sentences could markedly improve the performance of the translation models.

It is well-established that the preferred mode of communication of the deaf and hard of hearing (DHH) community are Sign Languages (SLs), but they are considered low resource languages where natural language processing technologies are of concern. In this paper we study the problem of text to SL gloss Machine Translation (MT) using Transformer-based architectures. Despite the significant advances of MT for spoken languages in the recent couple of decades, MT is in its infancy when it comes to SLs. We enrich a Transformer-based architecture aggregating syntactic information extracted from a dependency parser to word-embeddings. We test our model on a well-known dataset showing that the syntax-aware model obtains performance gains in terms of MT evaluation metrics.