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MultiCoNER: A large-scale multilingual dataset for complex named entity recognition

By Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko
2022
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We present MULTICONER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation. We applied two NER models on our dataset: a baseline XLM-RoBERTa model, and a state-of-the-art GEMNET model that leverages gazetteers. The baseline achieves moderate performance (macro-F1=54%), highlighting the difficulty of our data. GEMNET, which uses gazetteers, improvement significantly (average improvement of macro-F1=+30%). MULTICONER poses challenges even for large pre-trained language models, and we believe that it can help further research in building robust NER systems.

MULTICONER is publicly available, and we hope that this resource will help advance research in various aspects of NER.
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