When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages
Masha Medvedeva, Martin Kroon, Barbara Plank · 2017
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.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.