Abstract
SYSTRAN competes this year for the first time to the DSL shared task, in the Arabic Dialect Identification subtask. We participate by training several Neural Network models showing that we can obtain competitive results despite the limited amount of training data available for learning. We report our experiments and detail the network architecture and parameters of our 3 runs: our best performing system consists in a Multi-Input CNN that learns separate embeddings for lexical, phonetic and acoustic input features (F1: 0.5289); we also built a CNN-biLSTM network aimed at capturing both spatial and sequential features directly from speech spectrograms (F1: 0.3894 at submission time, F1: 0.4235 with later found parameters); and finally a system relying on binary CNN-biLSTMs (F1: 0.4339).- Anthology ID:
- W18-3914
- Volume:
- Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Marcos Zampieri, Preslav Nakov, Nikola Ljubešić, Jörg Tiedemann, Shervin Malmasi, Ahmed Ali
- Venue:
- VarDial
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 128–136
- Language:
- URL:
- https://aclanthology.org/W18-3914
- DOI:
- Cite (ACL):
- Elise Michon, Minh Quang Pham, Josep Crego, and Jean Senellart. 2018. Neural Network Architectures for Arabic Dialect Identification. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), pages 128–136, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Neural Network Architectures for Arabic Dialect Identification (Michon et al., VarDial 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W18-3914.pdf