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    <abstract>We present a machine learning approach for the Arabic Dialect Identification (ADI) and the German Dialect Identification (GDI) Closed Shared Tasks of the DSL 2017 Challenge. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided only for the Arabic data. In the learning stage, we independently employ Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR). Our approach is shallow and simple, but the empirical results obtained in the shared tasks prove that it achieves very good results. Indeed, we ranked on the first place in the ADI Shared Task with a weighted F1 score of 76.32% (4.62% above the second place) and on the fifth place in the GDI Shared Task with a weighted F1 score of 63.67% (2.57% below the first place).</abstract>
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%0 Conference Proceedings
%T Learning to Identify Arabic and German Dialects using Multiple Kernels
%A Ionescu, Radu Tudor
%A Butnaru, Andrei
%Y Nakov, Preslav
%Y Zampieri, Marcos
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shevin
%Y Ali, Ahmed
%S Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F ionescu-butnaru-2017-learning
%X We present a machine learning approach for the Arabic Dialect Identification (ADI) and the German Dialect Identification (GDI) Closed Shared Tasks of the DSL 2017 Challenge. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided only for the Arabic data. In the learning stage, we independently employ Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR). Our approach is shallow and simple, but the empirical results obtained in the shared tasks prove that it achieves very good results. Indeed, we ranked on the first place in the ADI Shared Task with a weighted F1 score of 76.32% (4.62% above the second place) and on the fifth place in the GDI Shared Task with a weighted F1 score of 63.67% (2.57% below the first place).
%R 10.18653/v1/W17-1225
%U https://aclanthology.org/W17-1225
%U https://doi.org/10.18653/v1/W17-1225
%P 200-209

Markdown (Informal)

[Learning to Identify Arabic and German Dialects using Multiple Kernels](https://aclanthology.org/W17-1225) (Ionescu & Butnaru, VarDial 2017)

• Learning to Identify Arabic and German Dialects using Multiple Kernels (Ionescu & Butnaru, VarDial 2017)

ACL

• Radu Tudor Ionescu and Andrei Butnaru. 2017. Learning to Identify Arabic and German Dialects using Multiple Kernels. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pages 200–209, Valencia, Spain. Association for Computational Linguistics.