DH-FBK at HODI: Multi-Task Learning with Classifier Ensemble Agreement, Oversampling and Synthetic Data

We describe the systems submitted by the DH-FBK team to the HODI shared task, dealing with Homotransphobia detection in Italian tweets (Subtask A) and prediction of the textual spans carrying the homotransphobic content (Explainability - Subtask B). We adopt a multi-task approach, developing a model able to solve both tasks at once and learn from different types of information. In our architecture, we fine-tuned an Italian BERT-model for detecting homotransphobic content as a classification task and, simultaneously, for locating the homotransphobic spans as a sequence labeling task. We also took into account the subjective nature of the task by artificially estimating the level of agreement among the annotators using a 5-classifier ensemble and incorporating this information in the multi-task setup. Moreover, we experimented by extending the initial training data with oversampling (Run 1) and via generation of synthetic data (Run2). Our runs achieve competitive results in both tasks. Finally, we conducted a series of additional experiments and a qualitative error analysis.