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

Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting