# Towards Privacy-Aware Sign Language Translation at Scale

arXiv: https://arxiv.org/abs/2402.09611 · HTML: https://mlsanity.com/paper/2402.09611/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 67 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://mlsanity.com/paper/2303.16727/) (2023)
- [Scaling Language-Image Pre-training via Masking](https://mlsanity.com/paper/2212.00794/) (2022)
- [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://mlsanity.com/paper/2306.00989/) (2023)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining](https://mlsanity.com/paper/2307.14768/) (2023)
- [A Study of Face Obfuscation in ImageNet](https://mlsanity.com/paper/2103.06191/) (2021)
- [YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus](https://mlsanity.com/paper/2306.15162/) (2023)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Sign Language Translation from Instructional Videos](https://mlsanity.com/paper/2304.06371/) (2023)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)

## Built on by (most cited first, 30 of 44)

- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues](https://mlsanity.com/paper/2501.09754/) (2025)
- [SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction](https://mlsanity.com/paper/2411.16765/) (2024)
- [SignMusketeers: An Efficient Multi-Stream Approach for Sign Language Translation at Scale](https://mlsanity.com/paper/2406.06907/) (2024)
- [SignRep: Enhancing Self-Supervised Sign Representations](https://mlsanity.com/paper/2503.08529/) (2025)
- [Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation](https://mlsanity.com/paper/2505.15438/) (2025)
- [Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation](https://mlsanity.com/paper/2506.00129/) (2025)
- [FLEURS-ASL: Including American Sign Language in Massively Multilingual Multitask Evaluation](https://mlsanity.com/paper/2408.13585/) (2024)
- [An Efficient Sign Language Translation Using Spatial Configuration and Motion Dynamics with LLMs](https://mlsanity.com/paper/2408.10593/) (2024)
- [Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field](https://mlsanity.com/paper/2603.13240/) (2026)
- [Scaling Sign Language Translation](https://mlsanity.com/paper/2407.11855/) (2024)
- [FSboard: Over 3 million characters of ASL fingerspelling collected via smartphones](https://mlsanity.com/paper/2407.15806/) (2024)
- [Sign Language Translation with Sentence Embedding Supervision](https://mlsanity.com/paper/2510.19367/) (2025)
- [Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment](https://mlsanity.com/paper/2512.08040/) (2025)
- [A Spatio-Temporal Representation Learning as an Alternative to Traditional Glosses in Sign Language Translation and Production](https://mlsanity.com/paper/2407.02854/) (2024)
- [MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation](https://mlsanity.com/paper/2601.19577/) (2026)
- [2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset](https://mlsanity.com/paper/2412.08274/) (2024)
- [Grounding or Guessing? Visual Signals for Detecting Hallucinations in Sign Language Translation](https://mlsanity.com/paper/2510.18439/) (2025)
- [SONAR-SLT: Multilingual Sign Language Translation via Language-Agnostic Sentence Embedding Supervision](https://mlsanity.com/paper/2510.19398/) (2025)
- [Pose-Based Sign Language Appearance Transfer](https://mlsanity.com/paper/2410.13675/) (2024)
- [RVLF: A Reinforcing Vision-Language Framework for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2512.07273/) (2025)
- [Sign Language Question Answering: A New Task, Benchmark, and Baseline for Sign Language Understanding](https://mlsanity.com/paper/2607.27826/) (2026)
- [Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audio](https://mlsanity.com/paper/2508.20476/) (2025)
- [SignBind-LLM: Multi-Stage Modality Fusion for Sign Language Translation](https://mlsanity.com/paper/2509.00030/) (2025)
- [SignMouth: Leveraging Mouthing Cues for Sign Language Translation by Multimodal Contrastive Fusion](https://mlsanity.com/paper/2509.10266/) (2025)
- [Saudi Sign Language Translation Using T5](https://mlsanity.com/paper/2510.11183/) (2025)
- [SignDATA: Data Pipeline for Sign Language Translation](https://mlsanity.com/paper/2604.20357/) (2026)
- [SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation](https://mlsanity.com/paper/2606.28626/) (2026)
- [SignLlama: Enhancing Gloss-free Sign Language Translation by Prioritizing Visual Features for LLMs](https://mlsanity.com/paper/2608.09006/) (2026)
- [Towards an AI-Driven Video-Based American Sign Language Dictionary: Exploring Design and Usage Experience with Learners](https://mlsanity.com/paper/2504.05857/) (2025)

## Abstract (arXiv)

A major impediment to the advancement of sign language translation (SLT) is data scarcity. Much of the sign language data currently available on the web cannot be used for training supervised models due to the lack of aligned captions. Furthermore, scaling SLT using large-scale web-scraped datasets bears privacy risks due to the presence of biometric information, which the responsible development of SLT technologies should account for. In this work, we propose a two-stage framework for privacy-aware SLT at scale that addresses both of these issues. We introduce SSVP-SLT, which leverages self-supervised video pretraining on anonymized and unannotated videos, followed by supervised SLT finetuning on a curated parallel dataset. SSVP-SLT achieves state-of-the-art finetuned and zero-shot gloss-free SLT performance on the How2Sign dataset, outperforming the strongest respective baselines by over 3 BLEU-4. Based on controlled experiments, we further discuss the advantages and limitations of self-supervised pretraining and anonymization via facial obfuscation for SLT.