KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media
Table 2: Macro averaged F1-Scores of our submissions and the other experiments on test data
['[BOLD] Model', '[BOLD] Arabic', '[BOLD] Greek', '[BOLD] Turkish', '[BOLD] Average']
Macro-averaged F1-Score metric was used for evaluation in this shared task. The baseline model used Term Frequency-Inverse Document Frequency (TF-IDF) with Support Vector Machine (SVM) Using CNNs with the same structure as the main model, but without pre-trained BERT as an embedder. CNN-Text model uses randomly initialized embeddings of size 300, which were trained along with the model. While CNNs could be used to capture local features of the text, LSTM which have shown remarkable performance in text classification tasks, capture the temporal information.