  We present a novel corpus for French dialect identification comprising
413,522 French text samples collected from public news websites in Belgium,
Canada, France and Switzerland. To ensure an accurate estimation of the dialect
identification performance of models, we designed the corpus to eliminate
potential biases related to topic, writing style, and publication source. More
precisely, the training, validation and test splits are collected from
different news websites, while searching for different keywords (topics). This
leads to a French cross-domain (FreCDo) dialect identification task. We conduct
experiments with four competitive baselines, a fine-tuned CamemBERT model, an
XGBoost based on fine-tuned CamemBERT features, a Support Vector Machines (SVM)
classifier based on fine-tuned CamemBERT features, and an SVM based on word
n-grams. Aside from presenting quantitative results, we also make an analysis
of the most discriminative features learned by CamemBERT. Our corpus is
available at https://github.com/MihaelaGaman/FreCDo.
