@inproceedings{elaswad-saqr-2026-articulator,
title = "Articulator-Level Cross-Lingual Transfer in Sign Language Translation",
author = "Elaswad, Ali and
Saqr, Omar Mokhtar Gaber",
editor = "Singh, Sanjeet and
Joshi, Abhinav and
Dattatreya, Pokala and
Artiaga, Keren and
Pujara, Khushi Jaydeep and
{\v{Z}}elezn{\'y}, Tom{\'a}{\v{s}} and
Gruber, Ivan and
Honz{\'i}k, Jakub and
Kamila, Sabyasachi and
Quiroga, Facundo Manuel and
Hr{\'u}z, Marek and
Hasanuzzaman, Mohammed and
Modi, Ashutosh",
booktitle = "Proceedings of the Second Workshop on Sign Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wslp-1.22/",
pages = "224--236",
ISBN = "979-8-89176-488-0",
abstract = "Cross-lingual transfer is a standard approach to low-resource sign language translation, but it is unclear what the transferred model actually provides. We fine-tune a pose-based model from American Sign Language (ASL) to Indian Sign Language (ISL) and decompose the resulting transfer across its encoder, decoder, and articulator streams. The matched ASL encoder-decoder pair reaches 3.61 BLEU-4 on ISL, while either component alone performs close to training from scratch. Content-word recall reveals a sharp frequency effect: frequently observed content improves with training and additional data, whereas rare and unseen content remains near zero, even after twenty epochs and a 49{\%} increase in training data. Finally, comparing ASL and Chinese Sign Language (CSL) sources shows that the apparent source-language gap is substantially reduced when the output language is controlled, with remaining differences concentrated differently across articulators. Because the source checkpoints also differ in pretraining domain, we treat this final pattern as suggestive rather than causal. Together, the results show that transfer provides a coupled encoder-decoder starting point, but does not by itself solve the long tail of target-language content."
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%0 Conference Proceedings
%T Articulator-Level Cross-Lingual Transfer in Sign Language Translation
%A Elaswad, Ali
%A Saqr, Omar Mokhtar Gaber
%Y Singh, Sanjeet
%Y Joshi, Abhinav
%Y Dattatreya, Pokala
%Y Artiaga, Keren
%Y Pujara, Khushi Jaydeep
%Y Železný, Tomáš
%Y Gruber, Ivan
%Y Honzík, Jakub
%Y Kamila, Sabyasachi
%Y Quiroga, Facundo Manuel
%Y Hrúz, Marek
%Y Hasanuzzaman, Mohammed
%Y Modi, Ashutosh
%S Proceedings of the Second Workshop on Sign Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-488-0
%F elaswad-saqr-2026-articulator
%X Cross-lingual transfer is a standard approach to low-resource sign language translation, but it is unclear what the transferred model actually provides. We fine-tune a pose-based model from American Sign Language (ASL) to Indian Sign Language (ISL) and decompose the resulting transfer across its encoder, decoder, and articulator streams. The matched ASL encoder-decoder pair reaches 3.61 BLEU-4 on ISL, while either component alone performs close to training from scratch. Content-word recall reveals a sharp frequency effect: frequently observed content improves with training and additional data, whereas rare and unseen content remains near zero, even after twenty epochs and a 49% increase in training data. Finally, comparing ASL and Chinese Sign Language (CSL) sources shows that the apparent source-language gap is substantially reduced when the output language is controlled, with remaining differences concentrated differently across articulators. Because the source checkpoints also differ in pretraining domain, we treat this final pattern as suggestive rather than causal. Together, the results show that transfer provides a coupled encoder-decoder starting point, but does not by itself solve the long tail of target-language content.
%U https://aclanthology.org/2026.wslp-1.22/
%P 224-236
Markdown (Informal)
[Articulator-Level Cross-Lingual Transfer in Sign Language Translation](https://aclanthology.org/2026.wslp-1.22/) (Elaswad & Saqr, WSLP 2026)
ACL