@inproceedings{imai-etal-2026-pragmatics,
title = "How Pragmatics Shape Articulation: A Computational Case Study in {STEM} {ASL} Discourse",
author = "Imai, Saki and
Kezar, Lee and
Aichler, Laurel and
Inan, Mert and
Walker, Erin and
Wooten, Alicia and
Quandt, Lorna Cobban and
Alikhani, Malihe",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.669/",
doi = "10.63317/2wjnaaabgz4d",
pages = "8476--8490",
abstract = "Most state-of-the-art sign language models are trained on interpreter or isolated vocabulary data, which overlooks the variability that characterizes natural dialogue. However, human communication dynamically adapts to contexts and interlocutors through spatiotemporal changes and articulation style. This specifically manifests itself in educational settings, where novel vocabularies are used by teachers, and students. To address this gap, we collect a motion capture dataset of American Sign Language (ASL) STEM (Science, Technology, Engineering, and Mathematics) dialogue that enables quantitative comparison between dyadic interactive signing, solo signed lecture, and interpreted articles. Using continuous kinematic features, we disentangle dialogue-specific entrainment from individual effort reduction and show spatiotemporal changes across repeated mentions of STEM terms. On average, dialogue signs are 24.6{\%}-44.6{\%} shorter in duration than the isolated signs, and show significant reductions absent in monologue contexts. Finally, we evaluate sign embedding models on their ability to recognize STEM signs and approximate how entrained the participants become over time. Our study bridges linguistic analysis and computational modeling to understand how pragmatics shape sign articulation and its representation in sign language technologies."
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<abstract>Most state-of-the-art sign language models are trained on interpreter or isolated vocabulary data, which overlooks the variability that characterizes natural dialogue. However, human communication dynamically adapts to contexts and interlocutors through spatiotemporal changes and articulation style. This specifically manifests itself in educational settings, where novel vocabularies are used by teachers, and students. To address this gap, we collect a motion capture dataset of American Sign Language (ASL) STEM (Science, Technology, Engineering, and Mathematics) dialogue that enables quantitative comparison between dyadic interactive signing, solo signed lecture, and interpreted articles. Using continuous kinematic features, we disentangle dialogue-specific entrainment from individual effort reduction and show spatiotemporal changes across repeated mentions of STEM terms. On average, dialogue signs are 24.6%-44.6% shorter in duration than the isolated signs, and show significant reductions absent in monologue contexts. Finally, we evaluate sign embedding models on their ability to recognize STEM signs and approximate how entrained the participants become over time. Our study bridges linguistic analysis and computational modeling to understand how pragmatics shape sign articulation and its representation in sign language technologies.</abstract>
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%0 Conference Proceedings
%T How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discourse
%A Imai, Saki
%A Kezar, Lee
%A Aichler, Laurel
%A Inan, Mert
%A Walker, Erin
%A Wooten, Alicia
%A Quandt, Lorna Cobban
%A Alikhani, Malihe
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F imai-etal-2026-pragmatics
%X Most state-of-the-art sign language models are trained on interpreter or isolated vocabulary data, which overlooks the variability that characterizes natural dialogue. However, human communication dynamically adapts to contexts and interlocutors through spatiotemporal changes and articulation style. This specifically manifests itself in educational settings, where novel vocabularies are used by teachers, and students. To address this gap, we collect a motion capture dataset of American Sign Language (ASL) STEM (Science, Technology, Engineering, and Mathematics) dialogue that enables quantitative comparison between dyadic interactive signing, solo signed lecture, and interpreted articles. Using continuous kinematic features, we disentangle dialogue-specific entrainment from individual effort reduction and show spatiotemporal changes across repeated mentions of STEM terms. On average, dialogue signs are 24.6%-44.6% shorter in duration than the isolated signs, and show significant reductions absent in monologue contexts. Finally, we evaluate sign embedding models on their ability to recognize STEM signs and approximate how entrained the participants become over time. Our study bridges linguistic analysis and computational modeling to understand how pragmatics shape sign articulation and its representation in sign language technologies.
%R 10.63317/2wjnaaabgz4d
%U https://aclanthology.org/2026.lrec-1.669/
%U https://doi.org/10.63317/2wjnaaabgz4d
%P 8476-8490
Markdown (Informal)
[How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discourse](https://aclanthology.org/2026.lrec-1.669/) (Imai et al., LREC 2026)
ACL
- Saki Imai, Lee Kezar, Laurel Aichler, Mert Inan, Erin Walker, Alicia Wooten, Lorna Cobban Quandt, and Malihe Alikhani. 2026. How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discourse. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 8476–8490, Palma de Mallorca, Spain. ELRA Language Resource Association.