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        <title>CASE 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned RoBERTa Document Embeddings</title>
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    <name type="personal">
        <namePart type="given">Samantha</namePart>
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            <title>Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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    <abstract>We present our submission to Task 2 of the Socio-political and Crisis Events Detection Shared Task at the CASE @ ACL-IJCNLP 2021 workshop. The task at hand aims at the fine-grained classification of socio-political events. Our best model was a fine-tuned RoBERTa transformer model using document embeddings. The corpus consisted of a balanced selection of sub-events extracted from the ACLED event dataset. We achieved a macro F-score of 0.923 and a micro F-score of 0.932 during our preliminary experiments on a held-out test set. The same model also performed best on the shared task test data (weighted F-score = 0.83). To analyze the results we calculated the topic compactness of the commonly misclassified events and conducted an error analysis.</abstract>
    <identifier type="citekey">kent-krumbiegel-2021-case</identifier>
    <identifier type="doi">10.18653/v1/2021.case-1.26</identifier>
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        <date>2021-08</date>
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            <start>208</start>
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%0 Conference Proceedings
%T CASE 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned RoBERTa Document Embeddings
%A Kent, Samantha
%A Krumbiegel, Theresa
%Y Hürriyetoğlu, Ali
%S Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F kent-krumbiegel-2021-case
%X We present our submission to Task 2 of the Socio-political and Crisis Events Detection Shared Task at the CASE @ ACL-IJCNLP 2021 workshop. The task at hand aims at the fine-grained classification of socio-political events. Our best model was a fine-tuned RoBERTa transformer model using document embeddings. The corpus consisted of a balanced selection of sub-events extracted from the ACLED event dataset. We achieved a macro F-score of 0.923 and a micro F-score of 0.932 during our preliminary experiments on a held-out test set. The same model also performed best on the shared task test data (weighted F-score = 0.83). To analyze the results we calculated the topic compactness of the commonly misclassified events and conducted an error analysis.
%R 10.18653/v1/2021.case-1.26
%U https://aclanthology.org/2021.case-1.26
%U https://doi.org/10.18653/v1/2021.case-1.26
%P 208-217

Markdown (Informal)

[CASE 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned RoBERTa Document Embeddings](https://aclanthology.org/2021.case-1.26) (Kent & Krumbiegel, CASE 2021)

• CASE 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned RoBERTa Document Embeddings (Kent & Krumbiegel, CASE 2021)

ACL

• Samantha Kent and Theresa Krumbiegel. 2021. CASE 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned RoBERTa Document Embeddings. In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 208–217, Online. Association for Computational Linguistics.