@inproceedings{kchaou-etal-2019-lium,
title = "{LIUM}-{MIRACL} Participation in the {MADAR} {A}rabic Dialect Identification Shared Task",
author = "Kchaou, Sam{\'e}h and
Bougares, Fethi and
Hadrich-Belguith, Lamia",
editor = "El-Hajj, Wassim and
Belguith, Lamia Hadrich and
Bougares, Fethi and
Magdy, Walid and
Zitouni, Imed and
Tomeh, Nadi and
El-Haj, Mahmoud and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4625",
doi = "10.18653/v1/W19-4625",
pages = "219--223",
abstract = "This paper describes the joint participation of the LIUM and MIRACL Laboratories at the Arabic dialect identification challenge of the MADAR Shared Task (Bouamor et al., 2019) conducted during the Fourth Arabic Natural Language Processing Workshop (WANLP 2019). We participated to the Travel Domain Dialect Identification subtask. We built several systems and explored different techniques including conventional machine learning methods and deep learning algorithms. Deep learning approaches did not perform well on this task. We experimented several classification systems and we were able to identify the dialect of an input sentence with an F1-score of 65.41{\%} on the official test set using only the training data supplied by the shared task organizers.",
}
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%0 Conference Proceedings
%T LIUM-MIRACL Participation in the MADAR Arabic Dialect Identification Shared Task
%A Kchaou, Saméh
%A Bougares, Fethi
%A Hadrich-Belguith, Lamia
%Y El-Hajj, Wassim
%Y Belguith, Lamia Hadrich
%Y Bougares, Fethi
%Y Magdy, Walid
%Y Zitouni, Imed
%Y Tomeh, Nadi
%Y El-Haj, Mahmoud
%Y Zaghouani, Wajdi
%S Proceedings of the Fourth Arabic Natural Language Processing Workshop
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F kchaou-etal-2019-lium
%X This paper describes the joint participation of the LIUM and MIRACL Laboratories at the Arabic dialect identification challenge of the MADAR Shared Task (Bouamor et al., 2019) conducted during the Fourth Arabic Natural Language Processing Workshop (WANLP 2019). We participated to the Travel Domain Dialect Identification subtask. We built several systems and explored different techniques including conventional machine learning methods and deep learning algorithms. Deep learning approaches did not perform well on this task. We experimented several classification systems and we were able to identify the dialect of an input sentence with an F1-score of 65.41% on the official test set using only the training data supplied by the shared task organizers.
%R 10.18653/v1/W19-4625
%U https://aclanthology.org/W19-4625
%U https://doi.org/10.18653/v1/W19-4625
%P 219-223
Markdown (Informal)
[LIUM-MIRACL Participation in the MADAR Arabic Dialect Identification Shared Task](https://aclanthology.org/W19-4625) (Kchaou et al., WANLP 2019)
ACL