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In this paper, we present a dataset for the computational study of a numberof Modern Greek dialects. It consists of raw text data from four dialects ofModern Greek, Cretan, Pontic, Northern Greek and Cypriot Greek. The dataset isof considerable size, albeit imbalanced, and presents the first attempt tocreate large scale dialectal resources of this type for Modern Greek dialects.We then use the dataset to perform dialect idefntification. We experiment withtraditional ML algorithms, as well as simple DL architectures. The results showvery good performance on the task, potentially revealing that the dialects inquestion have distinct enough characteristics allowing even simple ML models toperform well on the task. Error analysis is performed for the top performingalgorithms showing that in a number of cases the errors are due to insufficientdataset cleaning.

GRDD: A Dataset for Greek Dialectal NLP