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        <title>CASE 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models</title>
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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 introduce a method for the classification of texts into fine-grained categories of sociopolitical events. This particular method is responsive to all three Subtasks of Task 2, Fine-Grained Classification of Socio-Political Events, introduced at the CASE workshop of ACL-IJCNLP 2021. We frame Task 2 as textual entailment: given an input text and a candidate event class (“query”), the model predicts whether the text describes an event of the given type. The model is able to correctly classify in-sample event types with an average F1-score of 0.74 but struggles with some out-of-sample event types. Despite this, the model shows promise for the zero-shot identification of certain sociopolitical events by achieving an F1-score of 0.52 on one wholly out-of-sample event class.</abstract>
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%0 Conference Proceedings
%T CASE 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models
%A Radford, Benjamin J.
%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 radford-2021-case
%X We introduce a method for the classification of texts into fine-grained categories of sociopolitical events. This particular method is responsive to all three Subtasks of Task 2, Fine-Grained Classification of Socio-Political Events, introduced at the CASE workshop of ACL-IJCNLP 2021. We frame Task 2 as textual entailment: given an input text and a candidate event class (“query”), the model predicts whether the text describes an event of the given type. The model is able to correctly classify in-sample event types with an average F1-score of 0.74 but struggles with some out-of-sample event types. Despite this, the model shows promise for the zero-shot identification of certain sociopolitical events by achieving an F1-score of 0.52 on one wholly out-of-sample event class.
%R 10.18653/v1/2021.case-1.25
%U https://aclanthology.org/2021.case-1.25
%U https://doi.org/10.18653/v1/2021.case-1.25
%P 203-207

Markdown (Informal)

[CASE 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models](https://aclanthology.org/2021.case-1.25) (Radford, CASE 2021)

• CASE 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models (Radford, CASE 2021)

ACL

• Benjamin J. Radford. 2021. CASE 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models. In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 203–207, Online. Association for Computational Linguistics.