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    <abstract>Recent advances in end-to-end sign language translation (SLT) have achieved benchmark performance, yet little is known about whether these systems preserve the multi-channel linguistic structures that are essential for real-world communication. We argue that current optimization and evaluation practices create a form of linguistic invisibility, where interactionally decisive non-manual signals (NMS) are systematically underrepresented despite high translation scores.To empirically examine this issue, we analyze an interactional repair sequence from a Japanese Sign Language (JSL) conversational corpus as a diagnostic probe. Combining qualitative interactional analysis with kinematic measurements, we demonstrate a consistent manual–mouth decoupling pattern in which semantic resolution is carried primarily by mouthing while manual articulation remains largely constant. We show that such cross-channel contrast is unlikely to be preserved under current end-to-end training objectives that prioritize global motion similarity. Based on these findings, we argue that progress in SLT should be evaluated not only by sequence-level accuracy but also by the preservation of linguistically contrastive structures, motivating the development of diagnostic, multi-channel evaluation protocols for future SLT benchmarks. We therefore propose incorporating multi-channel diagnostic evaluation sets and decoupling-sensitive metrics into future SLT benchmarking frameworks, providing a pathway toward models that achieve both high performance and linguistic structural visibility.</abstract>
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%0 Conference Proceedings
%T Beyond BLEU: Linguistic Invisibility and Interactional Repair Sequence in End-to-End Sign Language Translation
%A Wang, Zirui
%A Bono, Mayumi
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F wang-bono-2026-beyond
%X Recent advances in end-to-end sign language translation (SLT) have achieved benchmark performance, yet little is known about whether these systems preserve the multi-channel linguistic structures that are essential for real-world communication. We argue that current optimization and evaluation practices create a form of linguistic invisibility, where interactionally decisive non-manual signals (NMS) are systematically underrepresented despite high translation scores.To empirically examine this issue, we analyze an interactional repair sequence from a Japanese Sign Language (JSL) conversational corpus as a diagnostic probe. Combining qualitative interactional analysis with kinematic measurements, we demonstrate a consistent manual–mouth decoupling pattern in which semantic resolution is carried primarily by mouthing while manual articulation remains largely constant. We show that such cross-channel contrast is unlikely to be preserved under current end-to-end training objectives that prioritize global motion similarity. Based on these findings, we argue that progress in SLT should be evaluated not only by sequence-level accuracy but also by the preservation of linguistically contrastive structures, motivating the development of diagnostic, multi-channel evaluation protocols for future SLT benchmarks. We therefore propose incorporating multi-channel diagnostic evaluation sets and decoupling-sensitive metrics into future SLT benchmarking frameworks, providing a pathway toward models that achieve both high performance and linguistic structural visibility.
%R 10.63317/3drmbqqsx7a8
%U https://aclanthology.org/2026.signlang-1.51/
%U https://doi.org/10.63317/3drmbqqsx7a8
%P 491-500

Markdown (Informal)

[Beyond BLEU: Linguistic Invisibility and Interactional Repair Sequence in End-to-End Sign Language Translation](https://aclanthology.org/2026.signlang-1.51/) (Wang & Bono, SignLang 2026)

• Beyond BLEU: Linguistic Invisibility and Interactional Repair Sequence in End-to-End Sign Language Translation (Wang & Bono, SignLang 2026)

ACL

• Zirui Wang and Mayumi Bono. 2026. Beyond BLEU: Linguistic Invisibility and Interactional Repair Sequence in End-to-End Sign Language Translation. In Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, pages 491–500, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).