Abstract
We describe several systems for identifying short samples of Arabic or Swiss-German dialects, which were prepared for the shared task of the 2017 DSL Workshop (Zampieri et al., 2017). The Arabic data comprises both text and acoustic files, and our best run combined both. The Swiss-German data is text-only. Coincidently, our best runs achieved a accuracy of nearly 63% on both the Swiss-German and Arabic dialects tasks.- Anthology ID:
- W17-1211
- 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:
- 93–101
- Language:
- URL:
- https://aclanthology.org/W17-1211
- DOI:
- 10.18653/v1/W17-1211
- Cite (ACL):
- Abualsoud Hanani, Aziz Qaroush, and Stephen Taylor. 2017. Identifying dialects with textual and acoustic cues. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pages 93–101, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Identifying dialects with textual and acoustic cues (Hanani et al., VarDial 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W17-1211.pdf