# Considerations for meaningful sign language machine translation based on glosses

arXiv: https://arxiv.org/abs/2211.15464 · HTML: https://mlsanity.com/paper/2211.15464/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation](https://mlsanity.com/paper/2107.10821/) (2021)
- [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [Evaluating Discourse Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1711.00513/) (2017)
- [Multi-channel Transformers for Multi-articulatory Sign Language Translation](https://mlsanity.com/paper/2009.00299/) (2020)
- [Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers](https://mlsanity.com/paper/2106.15195/) (2021)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Revisiting Low-Resource Neural Machine Translation: A Case Study](https://mlsanity.com/paper/1905.11901/) (2019)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation](https://mlsanity.com/paper/1905.10453/) (2019)
- [Data Augmentation for Sign Language Gloss Translation](https://mlsanity.com/paper/2105.07476/) (2021)
- [Ham2Pose: Animating Sign Language Notation into Pose Sequences](https://mlsanity.com/paper/2211.13613/) (2022)
- [Machine Translation from Signed to Spoken Languages: State of the Art and Challenges](https://mlsanity.com/paper/2202.03086/) (2022)
- [Neural Sign Language Translation by Learning Tokenization](https://mlsanity.com/paper/2002.00479/) (2020)
- [Changing the Representation: Examining Language Representation for Neural Sign Language Production](https://mlsanity.com/paper/2210.06312/) (2022)
- [Stronger Baselines for Trustable Results in Neural Machine Translation](https://mlsanity.com/paper/1706.09733/) (2017)

## Built on by (most cited first, 25 of 25)

- [Towards Privacy-Aware Sign Language Translation at Scale](https://mlsanity.com/paper/2402.09611/) (2024)
- [SLTUNET: A Simple Unified Model for Sign Language Translation](https://mlsanity.com/paper/2305.01778/) (2023)
- [Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator](https://mlsanity.com/paper/2411.17799/) (2024)
- [JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language Processing](https://mlsanity.com/paper/2311.10174/) (2023)
- [Towards Online Continuous Sign Language Recognition and Translation](https://mlsanity.com/paper/2401.05336/) (2024)
- [An Open-Source Gloss-Based Baseline for Spoken to Signed Language Translation](https://mlsanity.com/paper/2305.17714/) (2023)
- [Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations](https://mlsanity.com/paper/2309.12179/) (2023)
- [Multilingual Gloss-free Sign Language Translation: Towards Building a Sign Language Foundation Model](https://mlsanity.com/paper/2505.24355/) (2025)
- [iSign: A Benchmark for Indian Sign Language Processing](https://mlsanity.com/paper/2407.05404/) (2024)
- [Meaningful Pose-Based Sign Language Evaluation](https://mlsanity.com/paper/2510.07453/) (2025)
- [Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment](https://mlsanity.com/paper/2512.08040/) (2025)
- [Reconsidering Sentence-Level Sign Language Translation](https://mlsanity.com/paper/2406.11049/) (2024)
- [Generating Signed Language Instructions in Large-Scale Dialogue Systems](https://mlsanity.com/paper/2410.14026/) (2024)
- [SONAR-SLT: Multilingual Sign Language Translation via Language-Agnostic Sentence Embedding Supervision](https://mlsanity.com/paper/2510.19398/) (2025)
- [SignVLA: A Gloss-Free Vision-Language-Action Framework for Real-Time Sign Language-Guided Robotic Manipulation](https://mlsanity.com/paper/2602.22514/) (2026)
- [SignBLEU: Automatic Evaluation of Multi-channel Sign Language Translation](https://mlsanity.com/paper/2406.06648/) (2024)
- [ELMI: Interactive and Intelligent Sign Language Translation of Lyrics for Song Signing](https://mlsanity.com/paper/2409.09760/) (2024)
- [The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge](https://mlsanity.com/paper/2411.03568/) (2024)
- [MultimodalHugs: Enabling Sign Language Processing in Hugging Face](https://mlsanity.com/paper/2509.09729/) (2025)
- [Semi-Supervised Spoken Language Glossification](https://mlsanity.com/paper/2406.08173/) (2024)
- [Evaluation of a Sign Language Avatar on Comprehensibility, User Experience & Acceptability](https://mlsanity.com/paper/2508.05358/) (2025)
- [DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation](https://mlsanity.com/paper/2609.02796/) (2026)
- [Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes](https://mlsanity.com/paper/2609.18772/) (2026)
- [Machine Translation for Sign Languages](https://mlsanity.com/paper/2610.00881/) (2026)
- [A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition](https://mlsanity.com/paper/2610.14680/) (2026)

## Abstract (arXiv)

Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation. We find that limitations of glosses in general and limitations of specific datasets are not discussed in a transparent manner and that there is no common standard for evaluation. To address these issues, we put forward concrete recommendations for future research on gloss translation. Our suggestions advocate awareness of the inherent limitations of gloss-based approaches, realistic datasets, stronger baselines and convincing evaluation.