BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models
Table 1: Results of different models on validation part of Subtask 1 training data (first 3000 sentences). Results for RoBERTa and ALBERT models are averaged over ten training runs with the best found hyperparameters.
['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
For Subtask 1, we adapted the baseline provided by the task organizer to asses how more classical machine learning approaches perform on the dataset. After seeing the subpar performance, we turned our attention to pre-trained LRMs, namely RoBERTa and ALBERT.