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.

This paper describes a method for learning from a teacher’s potentially unreliable corrective feedback in an interactive task learning setting. The graphical model uses discourse coherence to jointly learn symbol grounding, domain concepts and valid plans. Our experiments show that the agent learns its domain-level task in spite of the teacher’s mistakes.