当稀疏传统模型优于密集神经网络:区分相似语言的奇怪案例

M. Medvedeva, Martin Kroon, Barbara Plank
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引用次数: 40

摘要

我们介绍了我们参与VarDial 4关于区分密切相关语言的共享任务的结果。我们的提交包括使用线性支持向量机(svm)和神经网络(NN)的简单传统模型。主要的想法是利用语言群体的信息。我们在传统模型中采用两层方法,在神经网络案例中采用多任务目标。我们的结果证实了早期的发现:简单的传统模型在这项任务中始终优于神经网络,至少考虑到我们可以在可用时间内检查的系统数量。我们的双层线性支持向量机在共享任务中排名第二。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages
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.
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