@inproceedings{alfter-appelgren-2025-grasp,
    title = "{GRASP} at {C}o{M}e{D}i Shared Task: Multi-Strategy Modeling of Annotator Behavior in Multi-Lingual Semantic Judgments",
    author = "Alfter, David  and
      Appelgren, Mattias",
    editor = "Roth, Michael  and
      Schlechtweg, Dominik",
    booktitle = "Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.comedi-1.7/",
    pages = "78--89",
    abstract = "This paper presents the GRASP team`s systems for the CoMeDi 2025 shared task on disagreement prediction in semantic annotation. The task comprises two subtasks: predicting median similarity scores and mean disagreement scores for word usage across multiple languages including Chinese, English, German, Norwegian, Russian, Spanish, and Swedish. For subtask 1, we implement three approaches: Prochain, a probabilistic chain model predicting sequential judgments; FARM, an ensemble of five fine-tuned XLM-RoBERTa models; and THAT, a task-specific model using XL-Lexeme with adaptive thresholds. For subtask 2, we develop three systems: LAMP, combining language-agnostic and monolingual models; BUMBLE, using optimal language combinations; and DRAMA, leveraging disagreement patterns from FARM`s outputs. Our results show strong performance across both subtasks, ranking second overall among participating teams. The probabilistic Prochain model demonstrates surprisingly robust performance when given accurate initial judgments, while our task-specific approaches show varying effectiveness across languages."
}