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.

此研究论文介绍了一个用于计算研究现代希腊方言的数据集，其中包括克里特岛、庇安提、北希腊和塞浦路斯希腊四种方言的原始文本数据。该数据集规模相当大，尽管存在不平衡问题，但是它是第一个针对现代希腊方言创造大规模方言资源的尝试。我们使用该数据集进行方言识别，并进行了传统机器学习算法和简单深度学习架构的实验。结果表明，这些方言在任务中具有足够独特的特征，即使是简单的机器学习模型也能表现出很好的性能。对表现最佳算法进行误差分析后发现，在一些情况下错误是由于数据集清理不足造成的。