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• W2626499725 abstract "We present the results of our participation in the VarDial 4 shared task on discriminating closely related languages. Our submission includes simple traditional models using linear support vector machines (SVMs) and a neural network (NN). The main idea was to leverage language group information. We did so with a two-layer approach in the traditional model and a multi-task objective in the neural network case. Our results confirm earlier findings: simple traditional models outperform neural networks consistently for this task, at least given the amount of systems we could examine in the available time. Our two-layer linear SVM ranked 2nd in the shared task." @default.
• W2626499725 created "2017-06-23" @default.
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• W2626499725 date "2017-01-01" @default.
• W2626499725 modified "2023-09-27" @default.
• W2626499725 title "When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages" @default.
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• W2626499725 doi "https://doi.org/10.18653/v1/w17-1219" @default.
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