  State-of-the-art sign language generation frameworks lack expressivity and
naturalness which is the result of only focusing manual signs, neglecting the
affective, grammatical and semantic functions of facial expressions. The
purpose of this work is to augment semantic representation of sign language
through grounding facial expressions. We study the effect of modeling the
relationship between text, gloss, and facial expressions on the performance of
the sign generation systems. In particular, we propose a Dual Encoder
Transformer able to generate manual signs as well as facial expressions by
capturing the similarities and differences found in text and sign gloss
annotation. We take into consideration the role of facial muscle activity to
express intensities of manual signs by being the first to employ facial action
units in sign language generation. We perform a series of experiments showing
that our proposed model improves the quality of automatically generated sign
language.
