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.

Online presence on social media platforms such as Facebook and Twitter has become a daily habit for internet users. Despite the vast amount of services the platforms offer for their users, users suffer from cyber-bullying, which further leads to mental abuse and may escalate to cause physical harm to individuals or targeted groups. In this paper, we present our submission to the Arabic Hate Speech 2022 Shared Task Workshop (OSACT5 2022) using the associated Arabic Twitter dataset. The Shared Task consists of 3 Sub-tasks, Sub-task A focuses on detecting whether the tweet is Offensive or not. Then, For offensive Tweets, Sub-task B focuses on detecting whether the tweet is Hate Speech or not. Finally, For Hate Speech Tweets, Sub-task C focuses on detecting the fine-grained type of hate speech among six different classes. Transformer models proved their efficiency in classification tasks, but with the problem of over-fitting when fine-tuned on a small or an imbalanced dataset. We overcome this limitation by investigating multiple training paradigms such as Contrastive learning and Multi-task learning along with classification fine-tuning and an ensemble of our top 5 performers. Our proposed solution achieved 0.841, 0.817, and 0.476 macro F1-average in sub-tasks A, B, and C respectively.

This paper presents our submissions to WMT 22 shared task in the Unsupervised and Very Low Resource Supervised Machine Translation tasks. The task revolves around translating between German ↔ Upper Sorbian (de ↔ hsb), German ↔ Lower Sorbian (de ↔ dsb) and Upper Sorbian ↔ Lower Sorbian (hsb ↔ dsb) in both unsupervised and supervised manner. For the unsupervised system, we trained an unsupervised phrase-based statistical machine translation (UPBSMT) system on each pair independently. We pretrained a De-Salvic mBART model on the following languages Polish (pl), Czech (cs), German (de), Upper Sorbian (hsb), Lower Sorbian (dsb). We then fine-tuned our mBART on the synthetic parallel data generated by the (UPBSMT) model along with authentic parallel data (de ↔ pl, de ↔ cs). We further fine-tuned our unsupervised system on authentic parallel data (hsb ↔ dsb, de ↔ dsb, de ↔ hsb) to submit our supervised low-resource system.