This paper presents work on novel machine translation (MT) systems between
spoken and signed languages, where signed languages are represented in
SignWriting, a sign language writing system. Our work seeks to address the lack
of out-of-the-box support for signed languages in current MT systems and is
based on the SignBank dataset, which contains pairs of spoken language text and
SignWriting content. We introduce novel methods to parse, factorize, decode,
and evaluate SignWriting, leveraging ideas from neural factored MT. In a
bilingual setup--translating from American Sign Language to (American)
English--our method achieves over 30 BLEU, while in two multilingual
setups--translating in both directions between spoken languages and signed
languages--we achieve over 20 BLEU. We find that common MT techniques used to
improve spoken language translation similarly affect the performance of sign
language translation. These findings validate our use of an intermediate text
representation for signed languages to include them in natural language
processing research.

本文研究了机器翻译系统在口语和手语之间的应用，使用手语书写系统 SignWriting 作为手语的表示方式。利用神经因子化机器翻译的方法，该方法能够在手语到英语的双语转换中达到 30 BLEU 的翻译效果，在口语和手语之间的双向翻译中达到 20 BLEU 的翻译效果。这些研究结果证实了中间文本表示法对手语的自然语言处理具有重要意义。