  This paper describes our efforts in tackling Task 5 of SemEval-2020. The task
involved detecting a class of textual expressions known as counterfactuals and
separating them into their constituent elements. Counterfactual statements
describe events that have not or could not have occurred and the possible
implications of such events. While counterfactual reasoning is natural for
humans, understanding these expressions is difficult for artificial agents due
to a variety of linguistic subtleties. Our final submitted approaches were an
ensemble of various fine-tuned transformer-based and CNN-based models for the
first subtask and a transformer model with dependency tree information for the
second subtask. We ranked 4-th and 9-th in the overall leaderboard. We also
explored various other approaches that involved the use of classical methods,
other neural architectures and the incorporation of different linguistic
features.
