Sign Language Translation

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Normativity and Productivism: Ableist Intelligence? A Degrowth Analysis of AI Sign Language Translation Tools for Deaf People
Apr 30, 2026Nina Seron-Abouelfadil, Poppy FynesSign Language Translation Algorithmic Bias
University of York
Targeted Linguistic Analysis of Sign Language Models with Minimal Translation Pairs
Apr 29, 2026Serpil Karabüklü, Kanishka Misra, Shester Gueuwou +3Sign Language Translation Sign Language Datasets
Toyota Technological Institute at Chicago · Linguistics Department, The University of Texas at Austin · Linguistics Department, The University of Chicago
Evaluation of Pose Estimation Systems for Sign Language Translation
Apr 27, 2026Catherine O'Brien, Gerard Sant, Mathias Müller +1Sign Language Translation Human Pose Estimation
Department of Computational Linguistics / University of Zurich, Switzerland
Selective Contrastive Learning For Gloss Free Sign Language Translation
Apr 24, 2026Changhao Lai, Rui Zhao, Xuewen Zhong +2Contrastive Learning Sign Language Translation
School of Informatics, Xiamen University, China · 2Key Lab of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian-Taiwan (XMU), Ministry of Culture and Tourism, China · 3National Language Resources Monitoring and Research Center for Education and Teaching Media, Xiamen University, China
SignDATA: Data Pipeline for Sign Language Translation
Apr 22, 2026Kuanwei Chen, Tingyi LinSign Language Translation Training Data Curation
Computer Science and Information Engineering National Central University, Zhongli, Taiwan · Electrical Engineering National Changhua University Of Education, Changhua, Taiwan
SignDPO: Multi-level Direct Preference Optimisation for Skeleton-based Gloss-free Sign Language Translation
Apr 20, 2026Muxin Pu, Xiao-Ming Wu, Mei Kuan Lim +3Sign Language Translation Direct Preference Optimization
Monash University · Nanyang Technological University
Think in Latent Thoughts: A New Paradigm for Gloss-Free Sign Language Translation
Apr 16, 2026Yiyang Jiang, Li Zhang, Xiao-Yong Wei +1Sign Language Translation Video-Language Models
The Hong Kong Polytechnic University · Sichuan University
FEA-SLT: A Gloss-Free End-to-End Framework for Facial-Expression-Aware Sign Language Translation
Jan 7, 2026Guobin Tu, Di WengSign Language Translation Multimodal Fusion
School of Software Technology, Zhejiang University
SignBind-LLM: Multi-Stage Modality Fusion for Sign Language Translation
Aug 20, 2025Marshall Thomas, Edward Fish, Richard BowdenSign Language Translation Multimodal Fusion
University of Surrey, Guildford, GU2 7XH, United Kingdom

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Normativity and Productivism: Ableist Intelligence? A Degrowth Analysis of AI Sign Language Translation Tools for Deaf People

Nina Seron-Abouelfadil, Poppy Fynes

University of York

Sign Language Translation Algorithmic Bias Sign Language Recognition

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Sign languages, of any geographical or accentual variation, understandably face continuous scrutiny under the ever present popularity of verbal dictation and audism. Through this, many potential problems arise with the current lack of accessible communication for those who rely on such sign languages for essential conversation. Such AI systems regularly take the form of recognition and interpretation models, designed to provide seamless and accurate translation. In reality these systems are built from biased data and created without any input from deaf communities. Such models are widely used and accepted by their hearing counterparts who remain ignorant to the inherent culture, semantics and colloquial language present in gestural language systems. This phenomenon is best analysed under the scope of The Technological System and Technological bluff by Ellul. Indeed, what is at play here is the standardization of language by technicians into what can be captured by technique: data, statistics, a mathematical language. For that AI technique to exist, sign language must be rationalized, in a search for profit that annihilates the conditions for communication and fails to capture the human experience of the deaf person. By that process, it presents normative effects, creating a model of Man, standardized, massified, and who has to adapt to the tool and technical milieu instead of the other way around, which we assume should have been the goal of such a technology. Technique thus reshapes what it means to be human, to submit deaf people to the goals of productivity and efficiency. In doing so, it exhibits clear counter productivity, alienating instead of emancipating, isolating instead of nourishing human relationships. Therefore this paper argues for the idea of AI as Ableist Intelligence, as such systems seek to emphasise the humiliated and marginalised nature of sign.

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