Sign language translation systems are complex and require many components. As
a result, it is very hard to compare methods across publications. We present an
open-source implementation of a text-to-gloss-to-pose-to-video pipeline
approach, demonstrating conversion from German to Swiss German Sign Language,
French to French Sign Language of Switzerland, and Italian to Italian Sign
Language of Switzerland. We propose three different components for the
text-to-gloss translation: a lemmatizer, a rule-based word reordering and
dropping component, and a neural machine translation system. Gloss-to-pose
conversion occurs using data from a lexicon for three different signed
languages, with skeletal poses extracted from videos. To generate a sentence,
the text-to-gloss system is first run, and the pose representations of the
resulting signs are stitched together.

本篇研究实现了一个从文字到手语的系统，并提出了三个不同的组件来实现文字到手语的转换，包括一个词形还原器、一个基于规则的单词重排和删除组件以及一个神经机器翻译系统。将手语姿势从视频中提取出来，用于三种不同的手语语言中的词汇表进行手语姿势转换。最后，将运行文字到手语系统，并将生成的手势姿势组合起来生成一句话。

口语转手语翻译开源基于词汇的基线

An Open-Source Gloss-Based Baseline for Spoken to Signed Language  Translation

In this paper, we propose a new method for calculating the output layer in
neural machine translation systems. The method is based on predicting a binary
code for each word and can reduce computation time/memory requirements of the
output layer to be logarithmic in vocabulary size in the best case. In
addition, we also introduce two advanced approaches to improve the robustness
of the proposed model: using error-correcting codes and combining softmax and
binary codes. Experiments on two English-Japanese bidirectional translation
tasks show proposed models achieve BLEU scores that approach the softmax, while
reducing memory usage to the order of less than 1/10 and improving decoding
speed on CPUs by x5 to x10.

本文提出了一种新的神经机器翻译系统输出层计算方法，基于预测每个单词的二进制码，可以在最佳情况下将输出层的计算时间 / 内存需求缩小到对数级别。此外，本文还介绍了使用纠错码和结合 softmax 和二进制码来提高鲁棒性的两种高级方法。两个英日双向翻译实验表明，所提出的模型在接近 softmax 的 BLEU 分数的同时，将内存使用率降低到不到 1/10，CPU 解码速度提高了 5 到 10 倍。