FreCDo: A Large Corpus for French Cross-Domain Dialect Identification
This provides a valuable resource for researchers working on French dialect identification, though it appears incremental as it applies existing methods to new data.
The authors tackled the problem of French dialect identification by creating a large cross-domain corpus of 413,522 text samples from four countries, and achieved strong performance with fine-tuned CamemBERT models, though specific accuracy numbers are not provided in the abstract.
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.