@inproceedings{samih-etal-2017-learning,
title = "Learning from Relatives: Unified Dialectal {A}rabic Segmentation",
author = "Samih, Younes and
Eldesouki, Mohamed and
Attia, Mohammed and
Darwish, Kareem and
Abdelali, Ahmed and
Mubarak, Hamdy and
Kallmeyer, Laura",
editor = "Levy, Roger and
Specia, Lucia",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-1043",
doi = "10.18653/v1/K17-1043",
pages = "432--441",
abstract = "Arabic dialects do not just share a common koin{\'e}, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other. In this paper we build a unified segmentation model where the training data for different dialects are combined and a single model is trained. The model yields higher accuracies than dialect-specific models, eliminating the need for dialect identification before segmentation. We also measure the degree of relatedness between four major Arabic dialects by testing how a segmentation model trained on one dialect performs on the other dialects. We found that linguistic relatedness is contingent with geographical proximity. In our experiments we use SVM-based ranking and bi-LSTM-CRF sequence labeling.",
}
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<abstract>Arabic dialects do not just share a common koiné, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other. In this paper we build a unified segmentation model where the training data for different dialects are combined and a single model is trained. The model yields higher accuracies than dialect-specific models, eliminating the need for dialect identification before segmentation. We also measure the degree of relatedness between four major Arabic dialects by testing how a segmentation model trained on one dialect performs on the other dialects. We found that linguistic relatedness is contingent with geographical proximity. In our experiments we use SVM-based ranking and bi-LSTM-CRF sequence labeling.</abstract>
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%0 Conference Proceedings
%T Learning from Relatives: Unified Dialectal Arabic Segmentation
%A Samih, Younes
%A Eldesouki, Mohamed
%A Attia, Mohammed
%A Darwish, Kareem
%A Abdelali, Ahmed
%A Mubarak, Hamdy
%A Kallmeyer, Laura
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F samih-etal-2017-learning
%X Arabic dialects do not just share a common koiné, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other. In this paper we build a unified segmentation model where the training data for different dialects are combined and a single model is trained. The model yields higher accuracies than dialect-specific models, eliminating the need for dialect identification before segmentation. We also measure the degree of relatedness between four major Arabic dialects by testing how a segmentation model trained on one dialect performs on the other dialects. We found that linguistic relatedness is contingent with geographical proximity. In our experiments we use SVM-based ranking and bi-LSTM-CRF sequence labeling.
%R 10.18653/v1/K17-1043
%U https://aclanthology.org/K17-1043
%U https://doi.org/10.18653/v1/K17-1043
%P 432-441
Markdown (Informal)
[Learning from Relatives: Unified Dialectal Arabic Segmentation](https://aclanthology.org/K17-1043) (Samih et al., CoNLL 2017)
ACL
- Younes Samih, Mohamed Eldesouki, Mohammed Attia, Kareem Darwish, Ahmed Abdelali, Hamdy Mubarak, and Laura Kallmeyer. 2017. Learning from Relatives: Unified Dialectal Arabic Segmentation. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 432–441, Vancouver, Canada. Association for Computational Linguistics.