A Dataset and Strong Baselines for Classification of Czech News Texts
Hynek Kydlíček Jindřich Libovický
Pre-trained models for Czech Natural Language Processing are often evaluated
on purely linguistic tasks (POS tagging, parsing, NER) and relatively simple
classification tasks such as sentiment classification or article classification
from a single news source. As an alternative, we present
CZEch~NEws~Classification~dataset (CZE-NEC), one of the largest Czech
classification datasets, composed of news articles from various sources
spanning over twenty years, which allows a more rigorous evaluation of such
models. We define four classification tasks: news source, news category,
inferred author's gender, and day of the week. To verify the task difficulty,
we conducted a human evaluation, which revealed that human performance lags
behind strong machine-learning baselines built upon pre-trained transformer
models. Furthermore, we show that language-specific pre-trained encoder
analysis outperforms selected commercially available large-scale generative
language models.