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        <title>Multi\²OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT</title>
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    <abstract>In this paper, we propose Multi²OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system with an efficient and effective argument extraction method. We use a query, key, and value setting inspired by the Multimodal Transformer to replace the previously used bidirectional long short-term memory architecture with multi-head attention. Multi²OIE outperforms existing sequence-labeling systems with high computational efficiency on two benchmark evaluation datasets, Re-OIE2016 and CaRB. Additionally, we apply the proposed method to multilingual open IE using multilingual BERT. Experimental results on new benchmark datasets introduced for two languages (Spanish and Portuguese) demonstrate that our model outperforms other multilingual systems without training data for the target languages.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.99</identifier>
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%0 Conference Proceedings
%T Multi\²OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
%A Ro, Youngbin
%A Lee, Yukyung
%A Kang, Pilsung
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ro-etal-2020-multi
%X In this paper, we propose Multi²OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system with an efficient and effective argument extraction method. We use a query, key, and value setting inspired by the Multimodal Transformer to replace the previously used bidirectional long short-term memory architecture with multi-head attention. Multi²OIE outperforms existing sequence-labeling systems with high computational efficiency on two benchmark evaluation datasets, Re-OIE2016 and CaRB. Additionally, we apply the proposed method to multilingual open IE using multilingual BERT. Experimental results on new benchmark datasets introduced for two languages (Spanish and Portuguese) demonstrate that our model outperforms other multilingual systems without training data for the target languages.
%R 10.18653/v1/2020.findings-emnlp.99
%U https://aclanthology.org/2020.findings-emnlp.99
%U https://doi.org/10.18653/v1/2020.findings-emnlp.99
%P 1107-1117

Markdown (Informal)

[Multiˆ2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT](https://aclanthology.org/2020.findings-emnlp.99) (Ro et al., Findings 2020)

• Multiˆ2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT (Ro et al., Findings 2020)

ACL

• Youngbin Ro, Yukyung Lee, and Pilsung Kang. 2020. Multiˆ2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1107–1117, Online. Association for Computational Linguistics.