Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning
In this paper, we present our submission for SemEval 2020 Task 4 - Commonsense Validation and Explanation (ComVE). The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don{'}t. ComVE comprises of three subtasks to challenge and test a system{'}s capability in understanding commonsense knowledge from various dimensions. Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing. We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc. Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision. Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement. In subtask A and B, we achieved 96{\%} and 94{\%} accuracy respectively standing at 4th position in both the subtasks.
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