Two Outliers at BEA 2025 Shared Task: Tutor Identity Classification using DiReC, a Two-Stage Disentangled Contrastive Representation

This paper presents DiReC (Disentangled Contrastive Representation), a novel two-stage framework designed to address the BEA 2025 Shared Task 5: Tutor Identity Classification.The task involves distinguishing between responses generated by nine different tutors, including both human educators and large language models (LLMs).DiReC leverages a disentangled representation learning approach, separating semantic content and stylistic features to improve tutor identification accuracy.In Stage 1, the model learns discriminative content representations using cross-entropy loss.In Stage 2, it applies supervised contrastive learning on style embeddings and introduces a disentanglement loss to enforce orthogonality between style and content spaces.Evaluated on the validation set, DiReC achieves strong performance, with a macro-F1 score of 0.9101 when combined with a CatBoost classifier and refined using the Hungarian algorithm.The system ranks third overall in the shared task with a macro-F1 score of 0.9172, demonstrating the effectiveness of disentangled representation learning for tutor identity classification.