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    <abstract>Vision-Language Models (VLMs) have shown strong generalization across multimodal tasks, but their capacity to handle sign language translation (SLT), which requires fine-grained spatiotemporal reasoning and linguistic understanding, remains unclear. In this study, we evaluate whether small VLMs (with łeq3B parameters) can perform SLT effectively. We perform supervised fine-tuning on four publicly available multilingual SLT datasets, including one German (DGS), two American (ASL), and one Indian (ISL), applying parameter-efficient LoRA to the language decoder while keeping the vision encoder frozen and training only the connector. To evaluate translation quality, we propose entity- and semantics-aware metrics tailored for SLT. We highlight the data imbalance issues present in the above widely used SLT datasets. Our analysis highlights the limitations in applying general-purpose VLMs to SLT, unlike their applicability in other tasks, and provides insights to inform future development of VLMs for SLP, which is essential for building inclusive AI applications.</abstract>
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%0 Conference Proceedings
%T Can Small Vision–Language Models Perform Sign Language Translation?
%A Chowdhury, Anal Roy
%A Sanyal, Debarshi Kumar
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chowdhury-sanyal-2026-small
%X Vision-Language Models (VLMs) have shown strong generalization across multimodal tasks, but their capacity to handle sign language translation (SLT), which requires fine-grained spatiotemporal reasoning and linguistic understanding, remains unclear. In this study, we evaluate whether small VLMs (with łeq3B parameters) can perform SLT effectively. We perform supervised fine-tuning on four publicly available multilingual SLT datasets, including one German (DGS), two American (ASL), and one Indian (ISL), applying parameter-efficient LoRA to the language decoder while keeping the vision encoder frozen and training only the connector. To evaluate translation quality, we propose entity- and semantics-aware metrics tailored for SLT. We highlight the data imbalance issues present in the above widely used SLT datasets. Our analysis highlights the limitations in applying general-purpose VLMs to SLT, unlike their applicability in other tasks, and provides insights to inform future development of VLMs for SLP, which is essential for building inclusive AI applications.
%R 10.18653/v1/2026.findings-acl.1609
%U https://aclanthology.org/2026.findings-acl.1609/
%U https://doi.org/10.18653/v1/2026.findings-acl.1609
%P 32150-32166

Markdown (Informal)

[Can Small Vision–Language Models Perform Sign Language Translation?](https://aclanthology.org/2026.findings-acl.1609/) (Chowdhury & Sanyal, Findings 2026)

• Can Small Vision–Language Models Perform Sign Language Translation? (Chowdhury & Sanyal, Findings 2026)

ACL

• Anal Roy Chowdhury and Debarshi Kumar Sanyal. 2026. Can Small Vision–Language Models Perform Sign Language Translation?. In Findings of the Association for Computational Linguistics: ACL 2026, pages 32150–32166, San Diego, California, United States. Association for Computational Linguistics.