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In this paper, we investigate a commonsense inference task that unifiesnatural language understanding and commonsense reasoning. We describe ourattempt at SemEval-2020 Task 4 competition: Commonsense Validation andExplanation (ComVE) challenge. We discuss several state-of-the-art deeplearning architectures for this challenge. Our system uses prepared labeledtextual datasets that were manually curated for three different naturallanguage inference subtasks. The goal of the first subtask is to test whether amodel can distinguish between natural language statements that make sense andthose that do not make sense. We compare the performance of several languagemodels and fine-tuned classifiers. Then, we propose a method inspired byquestion/answering tasks to treat a classification problem as a multiple choicequestion task to boost the performance of our experimental results (96.06%),which is significantly better than the baseline. For the second subtask, whichis to select the reason why a statement does not make sense, we stand withinthe first six teams (93.7%) among 27 participants with very competitiveresults. Our result for last subtask of generating reason against the nonsensestatement shows many potentials for future researches as we applied the mostpowerful generative model of language (GPT-2) with 6.1732 BLEU score amongfirst four teams.

CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task