FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection
This work addresses satire detection in French news, which is incremental as it adapts existing methods to a new language and domain.
The authors introduced FreSaDa, a French satire dataset of 11,570 news articles, and tackled cross-domain satire detection by ensuring distinct publication sources between training and test sets, achieving significant improvements with an unsupervised domain adaptation method.
In this paper, we introduce FreSaDa, a French Satire Data Set, which is composed of 11,570 articles from the news domain. In order to avoid reporting unreasonably high accuracy rates due to the learning of characteristics specific to publication sources, we divided our samples into training, validation and test, such that the training publication sources are distinct from the validation and test publication sources. This gives rise to a cross-domain (cross-source) satire detection task. We employ two classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (average of CamemBERT word embeddings). As an additional contribution, we present an unsupervised domain adaptation method based on regarding the pairwise similarities (given by the dot product) between the training samples and the validation samples as features. By including these domain-specific features, we attain significant improvements for both character n-grams and CamemBERT embeddings.