This paper presents the approach and findings of Averroes Team in the BEA 2025 Shared Task Track 1: Mistake Identification. Our system uses the multilingual understanding capabilities of general text embedding models. Our approach involves full-model fine-tuning, where both the pre-trained language model and the classification head are optimized to detect tutor recognition of student mistakes in educational dialogues. This end-to-end training enables the model to better capture subtle pedagogical cues, leading to improved contextual understanding. Evaluated on the official test set, our system achieved an exact macro-F_1 score of 0.7155 and an accuracy of 0.8675, securing third place among the participating teams. These results underline the effectiveness of task-specific optimization in enhancing model sensitivity to error recognition within interactive learning contexts.

Arabic banking intent detection represents a challenging problem across multiple dialects. It imposes generalization difficulties due to the scarcity of Arabic language and its dialects resources compared to English. We propose a methodology that leverages contrastive training to overcome this limitation. We also augmented the data with several dialects using a translation model. Our experiments demonstrate the ability of our approach in capturing linguistic nuances across different Arabic dialects as well as accurately differentiating between banking intents across diverse linguistic landscapes. This would enhance multi-dialect banking services in the Arab world with limited Arabic language resources. Using our proposed method we achieved second place on subtask 1 leaderboard of the AraFinNLP2024 shared task with micro-F1 score of 0.8762 on the test split.