Neural Sign Language Production (SLP) aims to automatically translate from
spoken language sentences to sign language videos. Historically the SLP task
has been broken into two steps; Firstly, translating from a spoken language
sentence to a gloss sequence and secondly, producing a sign language video
given a sequence of glosses. In this paper we apply Natural Language Processing
techniques to the first step of the SLP pipeline. We use language models such
as BERT and Word2Vec to create better sentence level embeddings, and apply
several tokenization techniques, demonstrating how these improve performance on
the low resource translation task of Text to Gloss. We introduce Text to
HamNoSys (T2H) translation, and show the advantages of using a phonetic
representation for sign language translation rather than a sign level gloss
representation. Furthermore, we use HamNoSys to extract the hand shape of a
sign and use this as additional supervision during training, further increasing
the performance on T2H. Assembling best practise, we achieve a BLEU-4 score of
26.99 on the MineDGS dataset and 25.09 on PHOENIX14T, two new state-of-the-art
baselines.

本文旨在将自然语言处理技术应用于 Sign Language Production 管道的第一步，介绍了一种基于音素表示而非基于 gloss 表示的 T2H 翻译方法，并使用 HamNoSys 提取标志的手型作为额外的监督来进一步提高性能，最终在两个数据集上均取得了 BLEU-4 得分的最佳表现。