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.- Anthology ID:
- W17-1219
- Volume:
- Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Preslav Nakov, Marcos Zampieri, Nikola Ljubešić, Jörg Tiedemann, Shevin Malmasi, Ahmed Ali
- Venue:
- VarDial
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 156–163
- Language:
- URL:
- https://aclanthology.org/W17-1219
- DOI:
- 10.18653/v1/W17-1219
- Cite (ACL):
- Maria Medvedeva, Martin Kroon, and Barbara Plank. 2017. When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pages 156–163, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages (Medvedeva et al., VarDial 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W17-1219.pdf