This article explores deep learning models for the automatic identification
of registers - text varieties such as news reports and discussion forums - in
web-based datasets across 16 languages. Web register (or genre) identification
would provide a robust solution for understanding the content of web-scale
datasets, which have become crucial in computational linguistics. Despite
recent advances, the potential of register classifiers on the noisy web remains
largely unexplored, particularly in multilingual settings and when targeting
the entire unrestricted web. We experiment with a range of deep learning models
using the new Multilingual CORE corpora, which includes 16 languages annotated
using a detailed, hierarchical taxonomy of 25 registers designed to cover the
entire unrestricted web. Our models achieve state-of-the-art results, showing
that a detailed taxonomy in a hierarchical multi-label setting can yield
competitive classification performance. However, all models hit a glass ceiling
at approximately 80% F1 score, which we attribute to the non-discrete nature of
web registers and the inherent uncertainty in labeling some documents. By
pruning ambiguous examples, we improve model performance to over 90%. Finally,
multilingual models outperform monolingual ones, particularly benefiting
languages with fewer training examples and smaller registers. Although a
zero-shot setting decreases performance by an average of 7%, these drops are
not linked to specific registers or languages. Instead, registers show
surprising similarity across languages.

该研究探索了深度学习模型在自动识别网络文本分类（如新闻报道和讨论论坛）方面的应用，尤其是在多语言环境下，该模型通过使用新的多语言核心语料库，通过对 25 个分类目录进行层次化注释，提高了分类性能。