IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals
This work addresses the difficulty of counterfactual understanding for artificial agents, but it is incremental as it builds on existing methods for a specific NLP task.
The paper tackled the problem of detecting and analyzing counterfactual statements in text, a challenge for AI due to linguistic subtleties, by using ensemble models and transformer-based approaches, achieving rankings of 4th and 9th in the SemEval-2020 Task 5 leaderboard.
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.