@inproceedings{elaraby-zahran-2019-character,
title = "A Character Level Convolutional {B}i{LSTM} for {A}rabic Dialect Identification",
author = "Elaraby, Mohamed and
Zahran, Ahmed",
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/W19-4636",
doi = "10.18653/v1/W19-4636",
pages = "274--278",
abstract = "In this paper, we describe CU-RAISA teamcontribution to the 2019Madar shared task2, which focused on Twitter User fine-grained dialect identification. Among par-ticipating teams, our system ranked the4th(with 61.54{\%}) F1-Macro measure. Our sys-tem is trained using a character level convo-lutional bidirectional long-short-term memorynetwork trained on 2k users{'} data. We showthat training on concatenated user tweets asinput is further superior to training on usertweets separately and assign user{'}s label on themode of user{'}s tweets{'} predictions.",
}
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%0 Conference Proceedings
%T A Character Level Convolutional BiLSTM for Arabic Dialect Identification
%A Elaraby, Mohamed
%A Zahran, Ahmed
%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 elaraby-zahran-2019-character
%X In this paper, we describe CU-RAISA teamcontribution to the 2019Madar shared task2, which focused on Twitter User fine-grained dialect identification. Among par-ticipating teams, our system ranked the4th(with 61.54%) F1-Macro measure. Our sys-tem is trained using a character level convo-lutional bidirectional long-short-term memorynetwork trained on 2k users’ data. We showthat training on concatenated user tweets asinput is further superior to training on usertweets separately and assign user’s label on themode of user’s tweets’ predictions.
%R 10.18653/v1/W19-4636
%U https://aclanthology.org/W19-4636
%U https://doi.org/10.18653/v1/W19-4636
%P 274-278
Markdown (Informal)
[A Character Level Convolutional BiLSTM for Arabic Dialect Identification](https://aclanthology.org/W19-4636) (Elaraby & Zahran, WANLP 2019)
ACL