Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments
Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Lyle Ungar
Abstract
Although prediction of dialects is an important language processing task, with a wide range of applications, existing work is largely limited to coarse-grained varieties. Inspired by geolocation research, we propose the novel task of Micro-Dialect Identification (MDI) and introduce MARBERT, a new language model with striking abilities to predict a fine-grained variety (as small as that of a city) given a single, short message. For modeling, we offer a range of novel spatially and linguistically-motivated multi-task learning models. To showcase the utility of our models, we introduce a new, large-scale dataset of Arabic micro-varieties (low-resource) suited to our tasks. MARBERT predicts micro-dialects with 9.9% F1, 76 better than a majority class baseline. Our new language model also establishes new state-of-the-art on several external tasks.- Anthology ID:
- 2020.emnlp-main.472
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5855–5876
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.472
- DOI:
- 10.18653/v1/2020.emnlp-main.472
- Cite (ACL):
- Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, and Lyle Ungar. 2020. Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5855–5876, Online. Association for Computational Linguistics.
- Cite (Informal):
- Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments (Abdul-Mageed et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.472.pdf
- Code
- UBC-NLP/microdialects
- Data
- ASTD