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：本文提出了一个用于计算研究多种现代希腊方言的数据集。该数据集包含来自现代希腊语四种方言（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。尽管存在数据不平衡问题，但该数据集规模可观，是首次为现代希腊方言构建大规模方言资源的尝试。随后，我们利用该数据集进行方言识别实验，尝试了传统机器学习算法及简单的深度学习架构。实验结果表明，该任务取得了优异性能，这可能揭示所研究的方言具有足够鲜明的特征，即使简单的机器学习模型也能在此任务中表现良好。通过对最优算法进行错误分析，我们发现部分错误源于数据集清洗不够充分。