The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild
This provides a new dataset for web genre identification, addressing challenges like machine-translated content, but it is incremental as it builds on existing annotation schemata for a specific domain.
The paper tackles the problem of automatic genre identification for web documents by introducing the GINCO dataset, based on 1,125 Slovenian web documents with 650,000 words, and shows that Transformer-based models achieve macro F1 scores around 0.58, significantly outperforming pre-Transformer models at 0.22.
This paper presents a new training dataset for automatic genre identification GINCO, which is based on 1,125 crawled Slovenian web documents that consist of 650 thousand words. Each document was manually annotated for genre with a new annotation schema that builds upon existing schemata, having primarily clarity of labels and inter-annotator agreement in mind. The dataset consists of various challenges related to web-based data, such as machine translated content, encoding errors, multiple contents presented in one document etc., enabling evaluation of classifiers in realistic conditions. The initial machine learning experiments on the dataset show that (1) pre-Transformer models are drastically less able to model the phenomena, with macro F1 metrics ranging around 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 monolingual models that were previously proven to be superior to multilingual models on standard NLP tasks.