Select and Reorder: A Novel Approach for Neural Sign Language Production
This addresses the problem of data scarcity for sign language translation, enabling more effective models in resource-constrained settings, though it appears incremental as it builds on existing methods with a novel two-step approach.
The paper tackles the challenge of sign language translation in low-resource settings by introducing Select and Reorder (S&R), which breaks translation into gloss selection and reordering steps using non-autoregressive decoding, achieving a 37.88% BLEU-1 improvement on the mDGS dataset.
Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets. This paper introduces Select and Reorder (S&R), a novel approach that addresses data scarcity by breaking down the translation process into two distinct steps: Gloss Selection (GS) and Gloss Reordering (GR). Our method leverages large spoken language models and the substantial lexical overlap between source spoken languages and target sign languages to establish an initial alignment. Both steps make use of Non-AutoRegressive (NAR) decoding for reduced computation and faster inference speeds. Through this disentanglement of tasks, we achieve state-of-the-art BLEU and Rouge scores on the Meine DGS Annotated (mDGS) dataset, demonstrating a substantial BLUE-1 improvement of 37.88% in Text to Gloss (T2G) Translation. This innovative approach paves the way for more effective translation models for sign languages, even in resource-constrained settings.