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Simple Search
Hits 1 – 8 of 8
1
NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task ...
Abdul-Mageed, Muhammad
;
Zhang, Chiyu
;
Elmadany, AbdelRahim
. - : arXiv, 2021
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2
NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task ...
Abdul-Mageed, Muhammad
;
Zhang, Chiyu
;
Bouamor, Houda
. - : arXiv, 2020
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3
Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments ...
Abdul-Mageed, Muhammad
;
Zhang, Chiyu
;
Elmadany, AbdelRahim
. - : arXiv, 2020
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4
NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task ...
The 28th International Conference on Computational Linguistics 2020
;
Abdul-Mageed, Muhammad
;
Bouamor, Houda
. - : Underline Science Inc., 2020
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5
AraNet: A Deep Learning Toolkit for Arabic Social Media ...
Abdul-Mageed, Muhammad
;
Zhang, Chiyu
;
Hashemi, Azadeh
. - : arXiv, 2019
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6
BERT-Based Arabic Social Media Author Profiling ...
Zhang, Chiyu
;
Abdul-Mageed, Muhammad
. - : arXiv, 2019
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7
Multi-Task Bidirectional Transformer Representations for Irony Detection ...
Zhang, Chiyu
;
Abdul-Mageed, Muhammad
. - : arXiv, 2019
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8
DiaNet: BERT and Hierarchical Attention Multi-Task Learning of Fine-Grained Dialect ...
Abdul-Mageed, Muhammad
;
Zhang, Chiyu
;
Elmadany, AbdelRahim
;
Rajendran, Arun
;
Ungar, Lyle
. - : arXiv, 2019
Abstract:
Prediction of language varieties and dialects is an important language processing task, with a wide range of applications. For Arabic, the native tongue of ~ 300 million people, most varieties remain unsupported. To ease this bottleneck, we present a very large scale dataset covering 319 cities from all 21 Arab countries. We introduce a hierarchical attention multi-task learning (HA-MTL) approach for dialect identification exploiting our data at the city, state, and country levels. We also evaluate use of BERT on the three tasks, comparing it to the MTL approach. We benchmark and release our data and models. ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
;
Machine Learning cs.LG
URL:
https://dx.doi.org/10.48550/arxiv.1910.14243
https://arxiv.org/abs/1910.14243
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