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This paper presents a new training dataset for automatic genre identificationGINCO, which is based on 1,125 crawled Slovenian web documents that consist of650 thousand words. Each document was manually annotated for genre with a newannotation schema that builds upon existing schemata, having primarily clarityof labels and inter-annotator agreement in mind. The dataset consists ofvarious challenges related to web-based data, such as machine translatedcontent, encoding errors, multiple contents presented in one document etc.,enabling evaluation of classifiers in realistic conditions. The initial machinelearning experiments on the dataset show that (1) pre-Transformer models aredrastically less able to model the phenomena, with macro F1 metrics rangingaround 0.22, while Transformer-based models achieve scores of around 0.58, and(2) multilingual Transformer models work as well on the task as the monolingualmodels that were previously proven to be superior to multilingual models onstandard NLP tasks.

The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild