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。

使用 SignWriting 表现手语的口语语言和手语之间的机器翻译

Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting

Signed languages are the primary means of communication for many deaf and
hard of hearing individuals. Since signed languages exhibit all the fundamental
linguistic properties of natural language, we believe that tools and theories
of Natural Language Processing (NLP) are crucial towards its modeling. However,
existing research in Sign Language Processing (SLP) seldom attempt to explore
and leverage the linguistic organization of signed languages. This position
paper calls on the NLP community to include signed languages as a research area
with high social and scientific impact. We first discuss the linguistic
properties of signed languages to consider during their modeling. Then, we
review the limitations of current SLP models and identify the open challenges
to extend NLP to signed languages. Finally, we urge (1) the adoption of an
efficient tokenization method; (2) the development of linguistically-informed
models; (3) the collection of real-world signed language data; (4) the
inclusion of local signed language communities as an active and leading voice
in the direction of research.

本篇论文呼吁自然语言处理 (NLP) 社群将手语作为高社会和科学影响力的研究领域之一，评估了手语的语言学特征，并探讨了如何扩展 NLP 到手语，并建议采用有效的标记方法、开发基于语言学的模型、收集真实世界的手语数据、将当地的手语社区作为研究方向的积极和领导性声音。