2019 · 117 citations · 22 references
ComputingArabicComputer ScienceDialectologyInformation RetrievalNatural Language ProcessingDialect ClassificationNamed Entity RecognitionCrowdsourced Dialect DataDialect AnnotationDialect Processing
The shared task is the first to target a large set of dialect labels at the city and country levels. The paper presents the results and findings of the MADAR Shared Task on Arabic fine‑grained dialect identification. The shared task, organized during the Fourth Arabic NLP Workshop collocated with ACL 2019, comprised two subtasks—MADAR Travel Domain Dialect Identification and MADAR Twitter User Dialect Identification—and used data created under the MADAR project. A total of 21 teams from 15 countries participated in the shared task.
In this paper, we present the results and findings of the MADAR Shared Task on Arabic Fine-Grained Dialect Identification. This shared task was organized as part of The Fourth Arabic Natural Language Processing Workshop, collocated with ACL 2019. The shared task includes two subtasks: the MADAR Travel Domain Dialect Identification subtask (Subtask 1) and the MADAR Twitter User Dialect Identification subtask (Subtask 2). This shared task is the first to target a large set of dialect labels at the city and country levels. The data for the shared task was created or collected under the Multi-Arabic Dialect Applications and Resources (MADAR) project. A total of 21 teams from 15 countries participated in the shared task.
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