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        <title>Multilingual Entity and Relation Extraction Dataset and Model</title>
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    <name type="personal">
        <namePart type="given">Alessandro</namePart>
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        <namePart type="given">Klaudia</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Michał</namePart>
        <namePart type="family">Satława</namePart>
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    <name type="personal">
        <namePart type="given">Piotr</namePart>
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    <abstract>We present a novel dataset and model for a multilingual setting to approach the task of Joint Entity and Relation Extraction. The SMiLER dataset consists of 1.1 M annotated sentences, representing 36 relations, and 14 languages. To the best of our knowledge, this is currently both the largest and the most comprehensive dataset of this type. We introduce HERBERTa, a pipeline that combines two independent BERT models: one for sequence classification, and the other for entity tagging. The model achieves micro F1 81.49 for English on this dataset, which is close to the current SOTA on CoNLL, SpERT.</abstract>
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        <date>2021-04</date>
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            <start>1946</start>
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%0 Conference Proceedings
%T Multilingual Entity and Relation Extraction Dataset and Model
%A Seganti, Alessandro
%A Firląg, Klaudia
%A Skowronska, Helena
%A Satława, Michał
%A Andruszkiewicz, Piotr
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F seganti-etal-2021-multilingual
%X We present a novel dataset and model for a multilingual setting to approach the task of Joint Entity and Relation Extraction. The SMiLER dataset consists of 1.1 M annotated sentences, representing 36 relations, and 14 languages. To the best of our knowledge, this is currently both the largest and the most comprehensive dataset of this type. We introduce HERBERTa, a pipeline that combines two independent BERT models: one for sequence classification, and the other for entity tagging. The model achieves micro F1 81.49 for English on this dataset, which is close to the current SOTA on CoNLL, SpERT.
%R 10.18653/v1/2021.eacl-main.166
%U https://aclanthology.org/2021.eacl-main.166
%U https://doi.org/10.18653/v1/2021.eacl-main.166
%P 1946-1955

Markdown (Informal)

[Multilingual Entity and Relation Extraction Dataset and Model](https://aclanthology.org/2021.eacl-main.166) (Seganti et al., EACL 2021)

• Multilingual Entity and Relation Extraction Dataset and Model (Seganti et al., EACL 2021)

ACL

• Alessandro Seganti, Klaudia Firląg, Helena Skowronska, Michał Satława, and Piotr Andruszkiewicz. 2021. Multilingual Entity and Relation Extraction Dataset and Model. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1946–1955, Online. Association for Computational Linguistics.