Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning

Vertika Srivastava, Sudeep Kumar Sahoo, Yeon Hyang Kim, Rohit R.r, Mayank Raj, Ajay Jaiswal
2020 Proceedings of the Fourteenth Workshop on Semantic Evaluation   unpublished
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
more » ... h 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. * * Equal Contribution This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/.
doi:10.18653/v1/2020.semeval-1.74 fatcat:s34kdrpamzea7m25hmrb2fltrq