@inproceedings{guggilla-2016-discrimination,
title = "Discrimination between Similar Languages, Varieties and Dialects using {CNN}- and {LSTM}-based Deep Neural Networks",
author = "Guggilla, Chinnappa",
editor = {Nakov, Preslav and
Zampieri, Marcos and
Tan, Liling and
Ljube{\v{s}}i{\'c}, Nikola and
Tiedemann, J{\"o}rg and
Malmasi, Shervin},
booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4824/",
pages = "185--194",
abstract = "In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29{\%} weighted-F1 accuracy in this sub-task using CNN approach using default network parameters."
}
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%0 Conference Proceedings
%T Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks
%A Guggilla, Chinnappa
%Y Nakov, Preslav
%Y Zampieri, Marcos
%Y Tan, Liling
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shervin
%S Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F guggilla-2016-discrimination
%X In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29% weighted-F1 accuracy in this sub-task using CNN approach using default network parameters.
%U https://aclanthology.org/W16-4824/
%P 185-194
Markdown (Informal)
[Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks](https://aclanthology.org/W16-4824/) (Guggilla, VarDial 2016)
ACL