@inproceedings{dhrangadhariya-etal-2023-hevs,
    title = "{HEVS}-{TUW} at {S}em{E}val-2023 Task 8: Ensemble of Language Models and Rule-based Classifiers for Claims Identification and {PICO} Extraction",
    author = {Dhrangadhariya, Anjani  and
      Kusa, Wojciech  and
      M{\"u}ller, Henning  and
      Hanbury, Allan},
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.246",
    doi = "10.18653/v1/2023.semeval-1.246",
    pages = "1776--1782",
    abstract = "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.",
}

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.
Dhrangadhariya, Anjani Kusa, Wojciech M{\"u}ller, Henning Hanbury, Allan
HEVS-TUW at SemEval-2023 Task 8: Ensemble of Language Models and Rule-based Classifiers for Claims Identification and PICO Extraction