@inproceedings{elnokrashy-alkhamissi-2024-context,
title = "A Context-Contrastive Inference Approach To Partial Diacritization",
author = "ElNokrashy, Muhammad and
AlKhamissi, Badr",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.8",
doi = "10.18653/v1/2024.arabicnlp-1.8",
pages = "89--101",
abstract = "Diacritization plays a pivotal role for meaning disambiguation and improving readability in Arabic texts. Efforts have long focused on marking every eligible character (Full Diacritization). Overlooked in comparison, Partial Diacritzation ({`}PD{`}) is the selection of a subset of characters to be annotated to aid comprehension only where needed. Research has indicated that excessive diacritic marks can hinder skilled readers{---}reducing reading speed and accuracy. We conduct a behavioral experiment and show that partially marked text is often easier to read than fully marked text, and sometimes easier than plain text. In this light, we introduce Context-Contrastive Partial Diacritization ({`}CCPD{`}){---}a novel approach to {`}PD{`} which integrates seamlessly with existing Arabic diacritization systems. {`}CCPD{`} processes each word twice, once with context and once without, and diacritizes only the characters with disparities between the two inferences. Further, we introduce novel indicators for measuring partial diacritization quality to help establish this as a machine learning task. Lastly, we introduce {`}TD2{`}, a Transformer-variant of an established model which offers a markedly different performance profile on our proposed indicators compared to all other known systems.",
}
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<abstract>Diacritization plays a pivotal role for meaning disambiguation and improving readability in Arabic texts. Efforts have long focused on marking every eligible character (Full Diacritization). Overlooked in comparison, Partial Diacritzation (‘PD‘) is the selection of a subset of characters to be annotated to aid comprehension only where needed. Research has indicated that excessive diacritic marks can hinder skilled readers—reducing reading speed and accuracy. We conduct a behavioral experiment and show that partially marked text is often easier to read than fully marked text, and sometimes easier than plain text. In this light, we introduce Context-Contrastive Partial Diacritization (‘CCPD‘)—a novel approach to ‘PD‘ which integrates seamlessly with existing Arabic diacritization systems. ‘CCPD‘ processes each word twice, once with context and once without, and diacritizes only the characters with disparities between the two inferences. Further, we introduce novel indicators for measuring partial diacritization quality to help establish this as a machine learning task. Lastly, we introduce ‘TD2‘, a Transformer-variant of an established model which offers a markedly different performance profile on our proposed indicators compared to all other known systems.</abstract>
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%0 Conference Proceedings
%T A Context-Contrastive Inference Approach To Partial Diacritization
%A ElNokrashy, Muhammad
%A AlKhamissi, Badr
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F elnokrashy-alkhamissi-2024-context
%X Diacritization plays a pivotal role for meaning disambiguation and improving readability in Arabic texts. Efforts have long focused on marking every eligible character (Full Diacritization). Overlooked in comparison, Partial Diacritzation (‘PD‘) is the selection of a subset of characters to be annotated to aid comprehension only where needed. Research has indicated that excessive diacritic marks can hinder skilled readers—reducing reading speed and accuracy. We conduct a behavioral experiment and show that partially marked text is often easier to read than fully marked text, and sometimes easier than plain text. In this light, we introduce Context-Contrastive Partial Diacritization (‘CCPD‘)—a novel approach to ‘PD‘ which integrates seamlessly with existing Arabic diacritization systems. ‘CCPD‘ processes each word twice, once with context and once without, and diacritizes only the characters with disparities between the two inferences. Further, we introduce novel indicators for measuring partial diacritization quality to help establish this as a machine learning task. Lastly, we introduce ‘TD2‘, a Transformer-variant of an established model which offers a markedly different performance profile on our proposed indicators compared to all other known systems.
%R 10.18653/v1/2024.arabicnlp-1.8
%U https://aclanthology.org/2024.arabicnlp-1.8
%U https://doi.org/10.18653/v1/2024.arabicnlp-1.8
%P 89-101
Markdown (Informal)
[A Context-Contrastive Inference Approach To Partial Diacritization](https://aclanthology.org/2024.arabicnlp-1.8) (ElNokrashy & AlKhamissi, ArabicNLP-WS 2024)
ACL