# Neural Sign Language Translation by Learning Tokenization

arXiv: https://arxiv.org/abs/2002.00479 · HTML: https://mlsanity.com/paper/2002.00479/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2020.

## Built on by (most cited first, 19 of 19)

- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Gloss Attention for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2307.07361/) (2023)
- [Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks](https://mlsanity.com/paper/2103.06982/) (2021)
- [Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives](https://mlsanity.com/paper/2107.11317/) (2021)
- [Sign Language Translation from Instructional Videos](https://mlsanity.com/paper/2304.06371/) (2023)
- [Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation](https://mlsanity.com/paper/2109.13318/) (2021)
- [SimulSLT: End-to-End Simultaneous Sign Language Translation](https://mlsanity.com/paper/2112.04228/) (2021)
- [Considerations for meaningful sign language machine translation based on glosses](https://mlsanity.com/paper/2211.15464/) (2022)
- [Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2403.12556/) (2024)
- [Machine Translation from Signed to Spoken Languages: State of the Art and Challenges](https://mlsanity.com/paper/2202.03086/) (2022)
- [Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization](https://mlsanity.com/paper/2506.15980/) (2025)
- [Towards the extraction of robust sign embeddings for low resource sign language recognition](https://mlsanity.com/paper/2306.17558/) (2023)
- [Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation](https://mlsanity.com/paper/2211.00526/) (2022)
- [Clean Text and Full-Body Transformer: Microsoft's Submission to the WMT22 Shared Task on Sign Language Translation](https://mlsanity.com/paper/2210.13326/) (2022)
- [Signformer is all you need: Towards Edge AI for Sign Language](https://mlsanity.com/paper/2411.12901/) (2024)
- [Better Sign Language Translation with Monolingual Data](https://mlsanity.com/paper/2304.10844/) (2023)
- [Co-creation for Sign Language Processing and Machine Translation](https://mlsanity.com/paper/2503.01553/) (2025)
- [Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation](https://mlsanity.com/paper/2609.07965/) (2026)
- [OneSign: Unifying Sign Language Understanding Tasks with One Model](https://mlsanity.com/paper/2609.33090/) (2026)

## Abstract (arXiv)

Sign Language Translation has attained considerable success recently, raising hopes for improved communication with the Deaf. A pre-processing step called tokenization improves the success of translations. Tokens can be learned from sign videos if supervised data is available. However, data annotation at the gloss level is costly, and annotated data is scarce. The paper utilizes Adversarial, Multitask, Transfer Learning to search for semi-supervised tokenization approaches without burden of additional labeling. It provides extensive experiments to compare all the methods in different settings to conduct a deeper analysis. In the case of no additional target annotation besides sentences, the proposed methodology attains 13.25 BLUE-4 and 36.28 ROUGE scores which improves the current state-of-the-art by 4 points in BLUE-4 and 5 points in ROUGE.