With the rise of online platforms, moderating harmful or offensive user-generated content has become increasingly critical. As manual moderation is infeasible at scale, machine learning models are widely used to support this process. However, subjective tasks, such as offensive language detection, often suffer from annotator disagreement, resulting in noisy supervision that hinders training and evaluation. We propose Weak Ensemble Learning (WEL), a novel framework that explicitly models annotator disagreement by constructing and aggregating weak predictors derived from diverse annotator perspectives. WEL enables robust learning from subjective and inconsistent labels without requiring annotator metadata. Experiments on four benchmark datasets show that WEL outperforms strong baselines across multiple metrics, demonstrating its effectiveness and flexibility across domains and annotation conditions.

This paper presents our system for the Learning with Disagreements (LeWiDi-2025) shared task (Leonardelli et al., 2025), which targets the challenges of interpretative variation in multilingual irony detection. We introduce a unified framework that models annotator disagreement through soft-label prediction, multilingual adaptation and robustness-oriented training. Our approach integrates tailored data augmentation strategies (i.e., lexical swaps, prompt-based reformulation and back-translation) with an ensemble learning scheme to enhance sensitivity to contextual and cultural nuances. To better align predictions with human-annotated probability distributions, we compare multiple loss functions, including cross-entropy, Kullback—Leibler divergence and L1 loss, the latter showing the strongest compatibility with the Average Manhattan Distance evaluation metric. Comprehensive ablation studies reveal that data augmentation and ensemble learning consistently improve performance across languages, with their combination delivering the largest gains. The results demonstrate the effectiveness of combining augmentation diversity, metric-compatible optimisation and ensemble aggregation for tackling interpretative variation in multilingual irony detection.