CHILab at HODI: A minimalist approach

Abstract

This technical report illustrates the system developed by the CHILab team for the competition HODI at EVALITA 2023. The key idea of the method we proposed for the HODI Subtask A - Homotransphobia detection, was to develop different systems arranged as suitable combinations of Pre-Trained Language Model (PTLM) for embedding extraction, neural architectures for further elaborations over the embeddings and a classifier. In particular dense layers, LSTM, BiLSTM and Transformers were used as neural architectures. The best performing system across the ones investigated in this report was made by embeddings extracted via AlBERTo coupled with a Transformer that reaches a macro-F1 score of 0.753.