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

arXiv: https://arxiv.org/abs/2201.03857 · HTML: https://mlsanity.com/paper/2201.03857/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 52 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Genre as Weak Supervision for Cross-lingual Dependency Parsing](https://mlsanity.com/paper/2109.04733/) (2021)
- [Beyond the English Web: Zero-Shot Cross-Lingual and Lightweight Monolingual Classification of Registers](https://mlsanity.com/paper/2102.07396/) (2021)

## Built on by (most cited first, 8 of 8)

- [ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre Identification](https://mlsanity.com/paper/2303.03953/) (2023)
- [Automatic register identification for the open web using multilingual deep learning](https://mlsanity.com/paper/2406.19892/) (2024)
- [AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification](https://mlsanity.com/paper/2408.09794/) (2024)
- [Controlling Out-of-Domain Gaps in LLMs for Genre Classification and Generated Text Detection](https://mlsanity.com/paper/2412.20595/) (2024)
- [BERT Goes Off-Topic: Investigating the Domain Transfer Challenge using Genre Classification](https://mlsanity.com/paper/2311.16083/) (2023)
- [State of the Art in Text Classification for South Slavic Languages: Fine-Tuning or Prompting?](https://mlsanity.com/paper/2511.07989/) (2025)
- [FuDoBa: Fusing Document and Knowledge Graph-based Representations with Bayesian Optimisation](https://mlsanity.com/paper/2507.06622/) (2025)
- [LLMs Know More Than Words: A Genre Study with Syntax, Metaphor & Phonetics](https://mlsanity.com/paper/2512.04957/) (2025)

## Abstract (arXiv)

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.