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        <title>InterosML@Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive Learning for Event Classification</title>
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            <title>Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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        <name type="personal">
            <namePart type="given">Hristo</namePart>
            <namePart type="family">Tanev</namePart>
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        <name type="personal">
            <namePart type="given">Vanni</namePart>
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        <name type="personal">
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            <namePart type="given">Erdem</namePart>
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            <namePart type="given">Milena</namePart>
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    <abstract>Causal events play a crucial role in explaining the intricate relationships between the causes and effects of events. However, comprehending causal events within discourse, text, or speech poses significant semantic challenges. We propose a contrastive learning-based method in this submission to the Causal News Corpus - Event Causality Shared Task 2023, with a specific focus on SubTask1 centered on causal event classification. In our approach we pre-train our base model using Supervised Contrastive (SuperCon) learning. Subsequently, we fine-tune the pre-trained model for the specific task of causal event classification. Our experimentation demonstrates the effectiveness of our method, achieving a competitive performance, and securing the 2nd position on the leaderboard with an F1-Score of 84.36.</abstract>
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%0 Conference Proceedings
%T InterosML@Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive Learning for Event Classification
%A Patel, Rajat
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Zavarella, Vanni
%Y Yeniterzi, Reyyan
%Y Yörük, Erdem
%Y Slavcheva, Milena
%S Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F patel-2023-interosml
%X Causal events play a crucial role in explaining the intricate relationships between the causes and effects of events. However, comprehending causal events within discourse, text, or speech poses significant semantic challenges. We propose a contrastive learning-based method in this submission to the Causal News Corpus - Event Causality Shared Task 2023, with a specific focus on SubTask1 centered on causal event classification. In our approach we pre-train our base model using Supervised Contrastive (SuperCon) learning. Subsequently, we fine-tune the pre-trained model for the specific task of causal event classification. Our experimentation demonstrates the effectiveness of our method, achieving a competitive performance, and securing the 2nd position on the leaderboard with an F1-Score of 84.36.
%U https://aclanthology.org/2023.case-1.8
%P 60-65

Markdown (Informal)

[InterosML@Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive Learning for Event Classification](https://aclanthology.org/2023.case-1.8) (Patel, CASE-WS 2023)

• InterosML@Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive Learning for Event Classification (Patel, CASE-WS 2023)

ACL

• Rajat Patel. 2023. InterosML@Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive Learning for Event Classification. In Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, pages 60–65, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.