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In this paper, we explore strategies to detect and evaluate counterfactualsentences. We describe our system for SemEval-2020 Task 5: Modeling CausalReasoning in Language: Detecting Counterfactuals. We use a BERT base model forthe classification task and build a hybrid BERT Multi-Layer Perceptron systemto handle the sequence identification task. Our experiments show that whileintroducing syntactic and semantic features does little in improving the systemin the classification task, using these types of features as cascaded linearinputs to fine-tune the sequence-delimiting ability of the model ensures itoutperforms other similar-purpose complex systems like BiLSTM-CRF in the secondtask. Our system achieves an F1 score of 85.00% in Task 1 and 83.90% in Task 2.

Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis