Dhrangadhariya, A., Kusa, W., Müller, H., & Hanbury, A. (2023). HEVS-TUW at SemEval-2023 Task 8: Ensemble of Language Models and Rule-based Classifiers for Claims Identification and PICO Extraction. In The 17th International Workshop on Semantic Evaluation (SemEval-2023). Proceedings of the Workshop (pp. 1776–1782). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.semeval-1.246
This paper describes the HEVS-TUW team submission to the SemEval-2023 Task 8: Causal Claims. We participated in two subtasks: (1) causal claims detection and (2) PIO identification. For subtask 1, we experimented with an ensemble of weakly supervised question detection and fine-tuned Transformer-based models. For subtask 2 of PIO frame extraction, we used a combination of deep representation learning and a rule-based approach. Our best model for subtask 1 ranks fourth with an F1-score of 65.77%. It shows moderate benefit from ensembling models pre-trained on independent categories. The results for subtask 2 warrant further investigation for improvement.
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Project title:
Domänen-spezifische Systeme für Informationsextraktion und -suche: 860721 (European Commission)
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