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Neural Sign Language Production (SLP) aims to automatically translate fromspoken language sentences to sign language videos. Historically the SLP taskhas been broken into two steps; Firstly, translating from a spoken languagesentence to a gloss sequence and secondly, producing a sign language videogiven a sequence of glosses. In this paper we apply Natural Language Processingtechniques to the first step of the SLP pipeline. We use language models suchas BERT and Word2Vec to create better sentence level embeddings, and applyseveral tokenization techniques, demonstrating how these improve performance onthe low resource translation task of Text to Gloss. We introduce Text toHamNoSys (T2H) translation, and show the advantages of using a phoneticrepresentation for sign language translation rather than a sign level glossrepresentation. Furthermore, we use HamNoSys to extract the hand shape of asign and use this as additional supervision during training, further increasingthe performance on T2H. Assembling best practise, we achieve a BLEU-4 score of26.99 on the MineDGS dataset and 25.09 on PHOENIX14T, two new state-of-the-artbaselines.

Changing the Representation: Examining Language Representation for Neural Sign Language Production