  In this work, we introduce the MOldavian and ROmanian Dialectal COrpus
(MOROCO), which is freely available for download at
https://github.com/butnaruandrei/MOROCO. The corpus contains 33564 samples of
text (with over 10 million tokens) collected from the news domain. The samples
belong to one of the following six topics: culture, finance, politics, science,
sports and tech. The data set is divided into 21719 samples for training, 5921
samples for validation and another 5924 samples for testing. For each sample,
we provide corresponding dialectal and category labels. This allows us to
perform empirical studies on several classification tasks such as (i) binary
discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect
multi-class categorization by topic and (iii) cross-dialect multi-class
categorization by topic. We perform experiments using a shallow approach based
on string kernels, as well as a novel deep approach based on character-level
convolutional neural networks containing Squeeze-and-Excitation blocks. We also
present and analyze the most discriminative features of our best performing
model, before and after named entity removal.
