BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble Learning
Table 3: Class-wise F1 scores on the development set
['[EMPTY]', '[BOLD] LR', '[BOLD] LSTM', '[BOLD] LSTM (Glove)', '[BOLD] CNN-LSTM (Glove)', '[BOLD] BERT', '[BOLD] BERT-LR', '[BOLD] RoBERTa']
Machine learning models (logistic regression) achieve a relatively lower micro averaged F1 score of 0.514 on the development set, but give non-zero scores for all the classes. Stacked LSTM networks with randomly initialized embeddings do not achieve any improvement (0.490) on the development-set and conceive near-zero scores for multiple classes as well. This is due to the vast vocabulary of this relatively smaller dataset. Consequently, on using pre-trained GloVe embeddings with the stacked LSTM networks, we achieve a significant improvement (0.52).