@inproceedings{inan-etal-2025-align,
title = "How to Align Multiple Signed Language Corpora for Better Sign-to-Sign Translations?",
author = "Inan, Mert and
Zhong, Yang and
Ganesh, Vidya and
Alikhani, Malihe",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.202/",
doi = "10.18653/v1/2025.naacl-long.202",
pages = "4003--4016",
ISBN = "979-8-89176-189-6",
abstract = "There are more than 300 documented signed languages worldwide, which are indispensable avenues for computational linguists to study cross-cultural and cross-linguistic factors that affect automatic sign understanding and generation. Yet, these are studied under critically low-resource settings, especially when examining multiple signed languages simultaneously. In this work, we hypothesize that a linguistically informed alignment algorithm can improve the results of sign-to-sign translation models. To this end, we first conduct a qualitative analysis of similarities and differences across three signed languages: American Sign Language (ASL), Chinese Sign Language (CSL), and German Sign Language (DGS). We then introduce a novel generation and alignment algorithm for translating one sign language to another, exploring Large Language Models (LLMs) as intermediary translators and paraphrasers. We also compile a dataset of sign-to-sign translation pairs between these signed languages. Our model trained on this dataset performs well on automatic metrics for sign-to-sign translation and generation. Our code and data will be available for the camera-ready version of the paper."
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<abstract>There are more than 300 documented signed languages worldwide, which are indispensable avenues for computational linguists to study cross-cultural and cross-linguistic factors that affect automatic sign understanding and generation. Yet, these are studied under critically low-resource settings, especially when examining multiple signed languages simultaneously. In this work, we hypothesize that a linguistically informed alignment algorithm can improve the results of sign-to-sign translation models. To this end, we first conduct a qualitative analysis of similarities and differences across three signed languages: American Sign Language (ASL), Chinese Sign Language (CSL), and German Sign Language (DGS). We then introduce a novel generation and alignment algorithm for translating one sign language to another, exploring Large Language Models (LLMs) as intermediary translators and paraphrasers. We also compile a dataset of sign-to-sign translation pairs between these signed languages. Our model trained on this dataset performs well on automatic metrics for sign-to-sign translation and generation. Our code and data will be available for the camera-ready version of the paper.</abstract>
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%0 Conference Proceedings
%T How to Align Multiple Signed Language Corpora for Better Sign-to-Sign Translations?
%A Inan, Mert
%A Zhong, Yang
%A Ganesh, Vidya
%A Alikhani, Malihe
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F inan-etal-2025-align
%X There are more than 300 documented signed languages worldwide, which are indispensable avenues for computational linguists to study cross-cultural and cross-linguistic factors that affect automatic sign understanding and generation. Yet, these are studied under critically low-resource settings, especially when examining multiple signed languages simultaneously. In this work, we hypothesize that a linguistically informed alignment algorithm can improve the results of sign-to-sign translation models. To this end, we first conduct a qualitative analysis of similarities and differences across three signed languages: American Sign Language (ASL), Chinese Sign Language (CSL), and German Sign Language (DGS). We then introduce a novel generation and alignment algorithm for translating one sign language to another, exploring Large Language Models (LLMs) as intermediary translators and paraphrasers. We also compile a dataset of sign-to-sign translation pairs between these signed languages. Our model trained on this dataset performs well on automatic metrics for sign-to-sign translation and generation. Our code and data will be available for the camera-ready version of the paper.
%R 10.18653/v1/2025.naacl-long.202
%U https://aclanthology.org/2025.naacl-long.202/
%U https://doi.org/10.18653/v1/2025.naacl-long.202
%P 4003-4016
Markdown (Informal)
[How to Align Multiple Signed Language Corpora for Better Sign-to-Sign Translations?](https://aclanthology.org/2025.naacl-long.202/) (Inan et al., NAACL 2025)
ACL