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

Taja Kuzman, Peter Rupnik, Nikola Ljubešić

Abstract
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 650,000 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.
Anthology ID:
2022.lrec-1.170
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1584–1594
Language:
URL:
https://aclanthology.org/2022.lrec-1.170
DOI:
Bibkey:
Cite (ACL):
Taja Kuzman, Peter Rupnik, and Nikola Ljubešić. 2022. The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1584–1594, Marseille, France. European Language Resources Association.
Cite (Informal):
The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild (Kuzman et al., LREC 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2022.lrec-1.170.pdf
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