CNRL at SemEval-2020 Task 5: Modelling Causal Reasoning in Language with Multi-Head Self-Attention Weights based Counterfactual Detection
Table 2: Data distribution across the splits (number of samples)
['[BOLD] Task', '[BOLD] Training', '[BOLD] Development', '[BOLD] Test']
For all our experiments, we use Binary Cross Entropy loss (for counterfactual detection) and Smooth L1 loss (for antecedent-consequent span regression) with Adam optimizer (with weight decay) to train our models. We use a 90-10 data split for training and development purposes respectively. We validate our models on F1 score (for counterfactual detection) and Smooth L1 loss (for antecedent-consequent detection). The evaluation metrics for the counterfactual detection task are the binary precision, recall and F1 scores.