@inproceedings{khalifa-etal-2024-picking,
title = "Picking Up Where the Linguist Left Off: Mapping Morphology to Phonology through Learning the Residuals",
author = "Khalifa, Salam and
Qaddoumi, Abdelrahim and
Broselow, Ellen and
Rambow, Owen",
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-1.22",
doi = "10.18653/v1/2024.arabicnlp-1.22",
pages = "258--264",
abstract = "Learning morphophonological mappings between the spoken form of a language and its underlying morphological structures is crucial for enriching resources for morphologically rich languages like Arabic. In this work, we focus on Egyptian Arabic as our case study and explore the integration of linguistic knowledge with a neural transformer model. Our approach involves learning to correct the residual errors from hand-crafted rules to predict the spoken form from a given underlying morphological representation. We demonstrate that using a minimal set of rules, we can effectively recover errors even in very low-resource settings.",
}
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<abstract>Learning morphophonological mappings between the spoken form of a language and its underlying morphological structures is crucial for enriching resources for morphologically rich languages like Arabic. In this work, we focus on Egyptian Arabic as our case study and explore the integration of linguistic knowledge with a neural transformer model. Our approach involves learning to correct the residual errors from hand-crafted rules to predict the spoken form from a given underlying morphological representation. We demonstrate that using a minimal set of rules, we can effectively recover errors even in very low-resource settings.</abstract>
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%0 Conference Proceedings
%T Picking Up Where the Linguist Left Off: Mapping Morphology to Phonology through Learning the Residuals
%A Khalifa, Salam
%A Qaddoumi, Abdelrahim
%A Broselow, Ellen
%A Rambow, Owen
%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 khalifa-etal-2024-picking
%X Learning morphophonological mappings between the spoken form of a language and its underlying morphological structures is crucial for enriching resources for morphologically rich languages like Arabic. In this work, we focus on Egyptian Arabic as our case study and explore the integration of linguistic knowledge with a neural transformer model. Our approach involves learning to correct the residual errors from hand-crafted rules to predict the spoken form from a given underlying morphological representation. We demonstrate that using a minimal set of rules, we can effectively recover errors even in very low-resource settings.
%R 10.18653/v1/2024.arabicnlp-1.22
%U https://aclanthology.org/2024.arabicnlp-1.22
%U https://doi.org/10.18653/v1/2024.arabicnlp-1.22
%P 258-264
Markdown (Informal)
[Picking Up Where the Linguist Left Off: Mapping Morphology to Phonology through Learning the Residuals](https://aclanthology.org/2024.arabicnlp-1.22) (Khalifa et al., ArabicNLP-WS 2024)
ACL