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        <title>Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction</title>
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        <namePart type="given">Elisa</namePart>
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    <name type="personal">
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    <abstract>Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. Multi-CrossRE is a machine translated version of CrossRE (Bassignana and Plank, 2022), with a sub-portion including more than 200 sentences in seven diverse languages checked by native speakers. We run a baseline model over the 26 new datasets and–as sanity check–over the 26 back-translations to English. Results on the back-translated data are consistent with the ones on the original English CrossRE, indicating high quality of the translation and the resulting dataset.</abstract>
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%0 Conference Proceedings
%T Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction
%A Bassignana, Elisa
%A Ginter, Filip
%A Pyysalo, Sampo
%A van der Goot, Rob
%A Plank, Barbara
%Y Alumäe, Tanel
%Y Fishel, Mark
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F bassignana-etal-2023-multi
%X Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. Multi-CrossRE is a machine translated version of CrossRE (Bassignana and Plank, 2022), with a sub-portion including more than 200 sentences in seven diverse languages checked by native speakers. We run a baseline model over the 26 new datasets and–as sanity check–over the 26 back-translations to English. Results on the back-translated data are consistent with the ones on the original English CrossRE, indicating high quality of the translation and the resulting dataset.
%U https://aclanthology.org/2023.nodalida-1.9
%P 80-85

Markdown (Informal)

[Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction](https://aclanthology.org/2023.nodalida-1.9) (Bassignana et al., NoDaLiDa 2023)

• Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction (Bassignana et al., NoDaLiDa 2023)

ACL

• Elisa Bassignana, Filip Ginter, Sampo Pyysalo, Rob van der Goot, and Barbara Plank. 2023. Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction. In Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 80–85, Tórshavn, Faroe Islands. University of Tartu Library.