  Wikipedia is a great source of general world knowledge which can guide NLP
models better understand their motivation to make predictions. Structuring
Wikipedia is the initial step towards this goal which can facilitate fine-grain
classification of articles. In this work, we introduce the Shinra 5-Language
Categorization Dataset (SHINRA-5LDS), a large multi-lingual and multi-labeled
set of annotated Wikipedia articles in Japanese, English, French, German, and
Farsi using Extended Named Entity (ENE) tag set. We evaluate the dataset using
the best models provided for ENE label set classification and show that the
currently available classification models struggle with large datasets using
fine-grained tag sets.
