@inproceedings{jiang-etal-2026-segment,
title = "Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing",
author = {Jiang, Zifan and
Jang, Youngjoon and
Momeni, Liliane and
Varol, G{\"u}l and
Ebling, Sarah and
Zisserman, Andrew},
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1401/",
pages = "30371--30384",
ISBN = "979-8-89176-390-6",
abstract = "The goal of this work is to develop a universal approach for aligning subtitles (i.e., spoken language text with corresponding timestamps) to continuous sign language videos. Prior approaches typically rely on end-to-end training tied to a specific language or dataset, which limits their generality. In contrast, our method Segment, Embed, and Align (SEA) provides a single framework that works across multiple languages and domains. SEA leverages two pretrained models: the first to segment a video sequence into individual signs and the second to embed each sign video clip into a shared latent space with text. Alignment is subsequently performed with a lightweight dynamic programming procedure that runs efficiently on CPU within a minute, even for hour-long episodes. SEA is flexible and can adapt to a wide range of scenarios, utilizing resources from small lexicons to large continuous corpora. Experiments on four sign language datasets demonstrate state-of-the-art alignment performance, highlighting the potential of SEA to generate high-quality parallel data for advancing sign language processing."
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<abstract>The goal of this work is to develop a universal approach for aligning subtitles (i.e., spoken language text with corresponding timestamps) to continuous sign language videos. Prior approaches typically rely on end-to-end training tied to a specific language or dataset, which limits their generality. In contrast, our method Segment, Embed, and Align (SEA) provides a single framework that works across multiple languages and domains. SEA leverages two pretrained models: the first to segment a video sequence into individual signs and the second to embed each sign video clip into a shared latent space with text. Alignment is subsequently performed with a lightweight dynamic programming procedure that runs efficiently on CPU within a minute, even for hour-long episodes. SEA is flexible and can adapt to a wide range of scenarios, utilizing resources from small lexicons to large continuous corpora. Experiments on four sign language datasets demonstrate state-of-the-art alignment performance, highlighting the potential of SEA to generate high-quality parallel data for advancing sign language processing.</abstract>
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%0 Conference Proceedings
%T Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing
%A Jiang, Zifan
%A Jang, Youngjoon
%A Momeni, Liliane
%A Varol, Gül
%A Ebling, Sarah
%A Zisserman, Andrew
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F jiang-etal-2026-segment
%X The goal of this work is to develop a universal approach for aligning subtitles (i.e., spoken language text with corresponding timestamps) to continuous sign language videos. Prior approaches typically rely on end-to-end training tied to a specific language or dataset, which limits their generality. In contrast, our method Segment, Embed, and Align (SEA) provides a single framework that works across multiple languages and domains. SEA leverages two pretrained models: the first to segment a video sequence into individual signs and the second to embed each sign video clip into a shared latent space with text. Alignment is subsequently performed with a lightweight dynamic programming procedure that runs efficiently on CPU within a minute, even for hour-long episodes. SEA is flexible and can adapt to a wide range of scenarios, utilizing resources from small lexicons to large continuous corpora. Experiments on four sign language datasets demonstrate state-of-the-art alignment performance, highlighting the potential of SEA to generate high-quality parallel data for advancing sign language processing.
%U https://aclanthology.org/2026.acl-long.1401/
%P 30371-30384
Markdown (Informal)
[Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing](https://aclanthology.org/2026.acl-long.1401/) (Jiang et al., ACL 2026)
ACL
- Zifan Jiang, Youngjoon Jang, Liliane Momeni, Gül Varol, Sarah Ebling, and Andrew Zisserman. 2026. Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 30371–30384, San Diego, California, United States. Association for Computational Linguistics.