CASE 2021 Task 2

Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models



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.

Cite this Paper (BibTeX)
@article{radford:20210620,
    author={Benjamin J. Radford},
    title={CASE 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models},
    journal={Proceedings of the CASE 2021, co-located with ACL-IJCNLP 2021},
    year={2021},
    volume={},
    number={},
    pages={},
    DOI={}}