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Sign Language Translation (SLT) is a challenging task due to its cross-domainnature, involving the translation of visual-gestural language to text. Manyprevious methods employ an intermediate representation, i.e., gloss sequences,to facilitate SLT, thus transforming it into a two-stage task of sign languagerecognition (SLR) followed by sign language translation (SLT). However, thescarcity of gloss-annotated sign language data, combined with the informationbottleneck in the mid-level gloss representation, has hindered the furtherdevelopment of the SLT task. To address this challenge, we propose a novelGloss-Free SLT based on Visual-Language Pretraining (GFSLT-VLP), which improvesSLT by inheriting language-oriented prior knowledge from pre-trained models,without any gloss annotation assistance. Our approach involves two stages: (i)integrating Contrastive Language-Image Pre-training (CLIP) with maskedself-supervised learning to create pre-tasks that bridge the semantic gapbetween visual and textual representations and restore masked sentences, and(ii) constructing an end-to-end architecture with an encoder-decoder-likestructure that inherits the parameters of the pre-trained Visual Encoder andText Decoder from the first stage. The seamless combination of these noveldesigns forms a robust sign language representation and significantly improvesgloss-free sign language translation. In particular, we have achievedunprecedented improvements in terms of BLEU-4 score on the PHOENIX14T dataset(>+5) and the CSL-Daily dataset (>+3) compared to state-of-the-art gloss-freeSLT methods. Furthermore, our approach also achieves competitive results on thePHOENIX14T dataset when compared with most of the gloss-based methods. Our codeis available at https://github.com/zhoubenjia/GFSLT-VLP.

Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining