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 this https URL.

本文介绍一个收集于比利时、加拿大、法国和瑞士的公共新闻网站上的包含 413,522 个法语文本样本的新颖语料库。该文章通过对不同的新闻网站检索不同的关键词，消除了主题、写作风格和发布来源等潜在偏见，从而建立了一个法语跨领域方言识别任务；作者也通过四个竞争基线、基于微调的 CamemBERT 模型、基于微调 CamemBERT 特征的 XGBoost、基于微调 CamemBERT 特征的 SVM 分类器和基于单词 n-grams 的 SVM 进行了实验；最后，作者还分析了 CamemBERT 学到的最有区分度的特征。