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In this paper, we propose Multi$^2$OIE, which performs open informationextraction (open IE) by combining BERT with multi-head attention. Our model isa sequence-labeling system with an efficient and effective argument extractionmethod. We use a query, key, and value setting inspired by the MultimodalTransformer to replace the previously used bidirectional long short-term memoryarchitecture with multi-head attention. Multi$^2$OIE outperforms existingsequence-labeling systems with high computational efficiency on two benchmarkevaluation datasets, Re-OIE2016 and CaRB. Additionally, we apply the proposedmethod to multilingual open IE using multilingual BERT. Experimental results onnew benchmark datasets introduced for two languages (Spanish and Portuguese)demonstrate that our model outperforms other multilingual systems withouttraining data for the target languages.

Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT