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 ...
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.
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