BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble Learning
Table 1: Results for the Span Identification sub-task on the development set (*Final submission)
['[BOLD] Embedding', '[BOLD] Span-level Metrics  [BOLD] Precision', '[BOLD] Span-level Metrics  [BOLD] Recall', '[BOLD] Span-level Metrics  [BOLD] F1', '[BOLD] Token-level Metrics  [BOLD] Precision', '[BOLD] Token-level Metrics  [BOLD] Recall', '[BOLD] Token-level Metrics  [BOLD] F1', '[BOLD] Token-level Metrics  [BOLD] Accuracy']
For the span-identification sub-task, our system ranks 22nd with a span-level F1 score of 0.387 on the final test-set leaderboard. Our model consistently achieves more than 94% token-level accuracy across all the embeddings. We get the best token-level F1 scores (0.815) with BERT and RoBERTa embeddings. The latter also outperforms all the other embeddings for the span-level metrics. Therefore, we use RoBERTa embeddings for the final submission to the task leaderboard. The performance for this sub-task can possibly be improved further by using trainable embeddings by fine-tuning their respective transformer-models.