Offensive Language Identification in Greek
Table 7: Results for offensive language detection for Deep Learning models with Greek word embeddings. For each model, Precision (P), Recall (R), and F1 are reported on all classes, and weighted averages. Macro-F1 is also listed (best in bold).
['[BOLD] Model', '[BOLD] Not Offensive  [BOLD] P', '[BOLD] Not Offensive  [BOLD] R', '[BOLD] Not Offensive  [BOLD] F1', '[BOLD] Offensive  [BOLD] P', '[BOLD] Offensive  [BOLD] R', '[BOLD] Offensive  [BOLD] F1', '[BOLD] Weighted Average  [BOLD] P', '[BOLD] Weighted Average  [BOLD] R', '[BOLD] Weighted Average  [BOLD] F1', '[BOLD] F1 Macro']
As we can see LSTM and GRU with Attention outperformed all the other models in-terms of macro-f1. Notably it outperformed all other classifical models and deep learning models in precision, recall and f1 for Offensive class as well as the Not Offensive class. However, fine tuning BERT-Base Multilingual Cased model did not achieve good results. For this task monolingual Greek word embeddings perform significantly better than the multilingual bert embeddings. LSTM and GRU with Attention can be considered as the best model trained for OGTD.