  In this paper, we describe our system for Task 4 of SemEval 2020, which
involves differentiating between natural language statements that confirm to
common sense and those that do not. The organizers propose three subtasks -
first, selecting between two sentences, the one which is against common sense.
Second, identifying the most crucial reason why a statement does not make
sense. Third, generating novel reasons for explaining the against common sense
statement. Out of the three subtasks, this paper reports the system description
of subtask A and subtask B. This paper proposes a model based on transformer
neural network architecture for addressing the subtasks. The novelty in work
lies in the architecture design, which handles the logical implication of
contradicting statements and simultaneous information extraction from both
sentences. We use a parallel instance of transformers, which is responsible for
a boost in the performance. We achieved an accuracy of 94.8% in subtask A and
89% in subtask B on the test set.
