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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.472.pdf
Video:
 https://slideslive.com/38939363
Code
 UBC-NLP/microdialects
Data
ASTD