newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda Classification
Table 1: Task SI Results on development set trained with 90 percent of training data.
['[BOLD] Model', '[BOLD] F1 Score', '[BOLD] Precision', '[BOLD] Recall']
For this task we experimented with the following, (i) Tagging Scheme: P/NP, BIO, BIOE and BIOES, (ii) Language Models: BERT (base uncased, large uncased), RoBERTa, GPT2, and SpanBERT, (iii) Concatenating different hidden layers of BERT to get token representation, (iv) Inclusion of linguistic features using POS tags of tokens. The evaluation metric used to compare is F1 score, which is calculated based on the overlap between the predicted and actual spans.