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.

This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 ”Detection of Propaganda Techniques in News Articles”. We participated in Task ”Technique Classification” (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.