mlsanityData to 14 Oct 2026

Co-creation for Sign Language Processing and Machine Translation

arXiv 2503.01553 ↗First posted 3 Mar 2025cs.CLcited by 0

Not on the map. Why?

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.

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

21 of 141 references.

References · 21

  1. 2021Improving Sign Language Translation with Monolingual Data by Sign Back-Translationcited by 179
  2. 2022Two-Stream Network for Sign Language Recognition and Translationcited by 123
  3. 2020Progressive Transformers for End-to-End Sign Language Productioncited by 120
  4. 2018Video-based Sign Language Recognition without Temporal Segmentationcited by 88
  5. 2022A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translationcited by 80
  6. 2020TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translationcited by 75
  7. 2018Neural Sign Language Translation based on Human Keypoint Estimationcited by 65
  8. 2024Style Vectors for Steering Generative Large Language Modelcited by 56
  9. 2021Content4All Open Research Sign Language Translation Datasetscited by 30
  10. 2021Data Augmentation for Sign Language Gloss Translationcited by 28
  11. 2021SimulSLT: End-to-End Simultaneous Sign Language Translationcited by 27
  12. 2021Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Networkcited by 26
9 more
  1. 2022Machine Translation from Signed to Spoken Languages: State of the Art and Challengescited by 20
  2. 2020Neural Sign Language Translation by Learning Tokenizationcited by 19
  3. 2023Investigating Agency of LLMs in Human-AI Collaboration Taskscited by 15
  4. 2023Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generationcited by 14
  5. 2018DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translationcited by 10
  6. 2021Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translationcited by 3
  7. 2022Clean Text and Full-Body Transformer: Microsoft's Submission to the WMT22 Shared Task on Sign Language Translationcited by 3
  8. 2022Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22cited by 3
  9. 2024Lessons in co-creation: the inconvenient truths of inclusive sign language technology developmentcited by 2

Built on by

Not cited yet.