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

翻译：本文提出一个用于现代希腊语若干方言计算研究的数据集。该数据集包含四种现代希腊方言（克里特方言、本都方言、北希腊方言和塞浦路斯希腊方言）的原始文本数据。尽管数据分布存在不平衡，但该数据集规模可观，是首次为现代希腊方言创建此类大规模方言资源。我们随后利用该数据集进行方言识别任务，尝试了传统机器学习算法及简单深度学习架构。实验结果表明该任务性能优异，揭示所研究方言具有足够显著的差异性特征，使得即使是简单的机器学习模型也能取得良好表现。针对最优算法进行的错误分析显示，部分错误源于数据集清洗不充分。