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多种方言计算研究的数据集。该数据集包含四个现代希腊语方言（克里特方言、本都方言、北部希腊方言和塞浦路斯希腊方言）的原始文本数据。尽管数据规模分布不均衡，但该数据集是首次为现代希腊语方言创建的大规模此类方言资源。我们进而利用该数据集进行方言识别任务，实验采用传统机器学习算法及简单深度学习架构。结果表明该任务取得了优异的性能，可能揭示相关方言具有足够鲜明的特征，即便是简单机器学习模型也能在该任务中表现良好。针对性能最优算法进行错误分析，发现部分错误源于数据集清洗不充分。