@inproceedings{weisberg-mitelman-etal-2024-code,
title = "Code-Switching and Back-Transliteration Using a Bilingual Model",
author = "Weisberg Mitelman, Daniel and
Dershowitz, Nachum and
Bar, Kfir",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-eacl.102",
pages = "1501--1511",
abstract = "The challenges of automated transliteration and code-switching{--}detection in Judeo-Arabic texts are addressed. We introduce two novel machine-learning models, one focused on transliterating Judeo-Arabic into Arabic, and another aimed at identifying non-Arabic words, predominantly Hebrew and Aramaic. Unlike prior work, our models are based on a bilingual Arabic-Hebrew language model, providing a unique advantage in capturing shared linguistic nuances. Evaluation results show that our models outperform prior solutions for the same tasks. As a practical contribution, we present a comprehensive pipeline capable of taking Judeo-Arabic text, identifying non-Arabic words, and then transliterating the Arabic portions into Arabic script. This work not only advances the state of the art but also offers a valuable toolset for making Judeo-Arabic texts more accessible to a broader Arabic-speaking audience.",
}
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%0 Conference Proceedings
%T Code-Switching and Back-Transliteration Using a Bilingual Model
%A Weisberg Mitelman, Daniel
%A Dershowitz, Nachum
%A Bar, Kfir
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F weisberg-mitelman-etal-2024-code
%X The challenges of automated transliteration and code-switching–detection in Judeo-Arabic texts are addressed. We introduce two novel machine-learning models, one focused on transliterating Judeo-Arabic into Arabic, and another aimed at identifying non-Arabic words, predominantly Hebrew and Aramaic. Unlike prior work, our models are based on a bilingual Arabic-Hebrew language model, providing a unique advantage in capturing shared linguistic nuances. Evaluation results show that our models outperform prior solutions for the same tasks. As a practical contribution, we present a comprehensive pipeline capable of taking Judeo-Arabic text, identifying non-Arabic words, and then transliterating the Arabic portions into Arabic script. This work not only advances the state of the art but also offers a valuable toolset for making Judeo-Arabic texts more accessible to a broader Arabic-speaking audience.
%U https://aclanthology.org/2024.findings-eacl.102
%P 1501-1511
Markdown (Informal)
[Code-Switching and Back-Transliteration Using a Bilingual Model](https://aclanthology.org/2024.findings-eacl.102) (Weisberg Mitelman et al., Findings 2024)
ACL