MultiMUC: Multilingual Template Filling on MUC-4

Anonymous

Abstract: We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings.

Paper Type: long

Research Area: Information Extraction

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: Arabic,Chinese,English,Farsi,Korean,Russian

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