  This article presents an original method for Text-to-Sign Translation. It
compensates data scarcity using a domain-specific parallel corpus of alignments
between text and hierarchical formal descriptions of Sign Language videos in
AZee. Based on the detection of similarities present in the source text, the
proposed algorithm recursively exploits matches and substitutions of aligned
segments to build multiple candidate translations for a novel statement. This
helps preserving Sign Language structures as much as possible before falling
back on literal translations too quickly, in a generative way. The resulting
translations are in the form of AZee expressions, designed to be used as input
to avatar synthesis systems. We present a test set tailored to showcase its
potential for expressiveness and generation of idiomatic target language, and
observed limitations. This work finally opens prospects on how to evaluate
translation and linguistic aspects, such as accuracy and grammatical fluency.
