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.

本文提出了一种基于 AZee 的文本到手语翻译的算法，利用特定领域的平行语料库来解决数据短缺的问题，通过检测源文本中存在的相似性，递归地利用匹配和对齐段的替换来建立一个句子的多个候选翻译，这有助于以一种生成的方式尽可能保留手语结构。最终的翻译以 AZee 表达式的形式呈现，旨在作为输入到 Avatar 合成系统中。我们提供了一个测试集来展示其表现，并观察到了一些局限性。这项工作最终开启了评估翻译和语言方面的可能性，例如准确性和语法流畅性。