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.- Anthology ID:
- 2021.case-1.26
- Volume:
- Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- CASE
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 208–217
- Language:
- URL:
- https://aclanthology.org/2021.case-1.26
- DOI:
- 10.18653/v1/2021.case-1.26
- Cite (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.
- Cite (Informal):
- CASE 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned RoBERTa Document Embeddings (Kent & Krumbiegel, CASE 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.case-1.26.pdf