@inproceedings{tudoreanu-2019-dteam,
title = "{DT}eam @ {V}ar{D}ial 2019: Ensemble based on skip-gram and triplet loss neural networks for {M}oldavian vs. {R}omanian cross-dialect topic identification",
author = "Tudoreanu, Diana",
editor = {Zampieri, Marcos and
Nakov, Preslav and
Malmasi, Shervin and
Ljube{\v{s}}i{\'c}, Nikola and
Tiedemann, J{\"o}rg and
Ali, Ahmed},
booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
month = jun,
year = "2019",
address = "Ann Arbor, Michigan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1422",
doi = "10.18653/v1/W19-1422",
pages = "202--208",
abstract = "This paper presents the solution proposed by DTeam in the VarDial 2019 Evaluation Campaign for the Moldavian vs. Romanian cross-topic identification task. The solution proposed is a Support Vector Machines (SVM) ensemble composed of a two character-level neural networks. The first network is a skip-gram classification model formed of an embedding layer, three convolutional layers and two fully-connected layers. The second network has a similar architecture, but is trained using the triplet loss function.",
}
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%0 Conference Proceedings
%T DTeam @ VarDial 2019: Ensemble based on skip-gram and triplet loss neural networks for Moldavian vs. Romanian cross-dialect topic identification
%A Tudoreanu, Diana
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Malmasi, Shervin
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Ali, Ahmed
%S Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2019
%8 June
%I Association for Computational Linguistics
%C Ann Arbor, Michigan
%F tudoreanu-2019-dteam
%X This paper presents the solution proposed by DTeam in the VarDial 2019 Evaluation Campaign for the Moldavian vs. Romanian cross-topic identification task. The solution proposed is a Support Vector Machines (SVM) ensemble composed of a two character-level neural networks. The first network is a skip-gram classification model formed of an embedding layer, three convolutional layers and two fully-connected layers. The second network has a similar architecture, but is trained using the triplet loss function.
%R 10.18653/v1/W19-1422
%U https://aclanthology.org/W19-1422
%U https://doi.org/10.18653/v1/W19-1422
%P 202-208
Markdown (Informal)
[DTeam @ VarDial 2019: Ensemble based on skip-gram and triplet loss neural networks for Moldavian vs. Romanian cross-dialect topic identification](https://aclanthology.org/W19-1422) (Tudoreanu, VarDial 2019)
ACL