# Co-creation for Sign Language Processing and Machine Translation

arXiv: https://arxiv.org/abs/2503.01553 · HTML: https://mlsanity.com/paper/2503.01553/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 141 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 21 of 21)

- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020)
- [Video-based Sign Language Recognition without Temporal Segmentation](https://mlsanity.com/paper/1801.10111/) (2018)
- [A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation](https://mlsanity.com/paper/2203.04287/) (2022)
- [TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation](https://mlsanity.com/paper/2010.05468/) (2020)
- [Neural Sign Language Translation based on Human Keypoint Estimation](https://mlsanity.com/paper/1811.11436/) (2018)
- [Style Vectors for Steering Generative Large Language Model](https://mlsanity.com/paper/2402.01618/) (2024)
- [Content4All Open Research Sign Language Translation Datasets](https://mlsanity.com/paper/2105.02351/) (2021)
- [Data Augmentation for Sign Language Gloss Translation](https://mlsanity.com/paper/2105.07476/) (2021)
- [SimulSLT: End-to-End Simultaneous Sign Language Translation](https://mlsanity.com/paper/2112.04228/) (2021)
- [Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network](https://mlsanity.com/paper/2111.07258/) (2021)
- [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)
- [Investigating Agency of LLMs in Human-AI Collaboration Tasks](https://mlsanity.com/paper/2305.12815/) (2023)
- [Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation](https://mlsanity.com/paper/2310.10706/) (2023)
- [DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation](https://mlsanity.com/paper/1802.07584/) (2018)
- [Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation](https://mlsanity.com/paper/2102.06143/) (2021)
- [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)
- [Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22](https://mlsanity.com/paper/2212.01140/) (2022)
- [Lessons in co-creation: the inconvenient truths of inclusive sign language technology development](https://mlsanity.com/paper/2408.13171/) (2024)

## Abstract (arXiv)

Sign language machine translation (SLMT) -- the task of automatically translating between sign and spoken languages or between sign languages -- is a complex task within the field of NLP. Its multi-modal and non-linear nature require the joint efforts of sign language (SL) linguists, technical experts and SL users. Effective user involvement is a challenge that can be addressed through co-creation. Co-creation has been formally defined in many fields, e.g. business, marketing, educational and others, however in NLP and in particular in SLMT there is no formal, widely accepted definition. Starting from the inception and evolution of co-creation across various fields over time, we develop a relationship typology to address the collaboration between deaf, Hard of Hearing and hearing researchers and the co-creation with SL-users. We compare this new typology to the guiding principles of participatory design for NLP. We, then, assess 110 articles from the perspective of involvement of SL users and highlight the lack of involvement of the sign language community or users in decision-making processes required for effective co-creation. Finally, we derive formal guidelines for co-creation for SLMT which take the dynamic nature of co-creation throughout the life cycle of a research project into account.