In this paper, we investigate a commonsense inference task that unifies
natural language understanding and commonsense reasoning. We describe our
attempt at SemEval-2020 Task 4 competition: Commonsense Validation and
Explanation (ComVE) challenge. We discuss several state-of-the-art deep
learning architectures for this challenge. Our system uses prepared labeled
textual datasets that were manually curated for three different natural
language inference subtasks. The goal of the first subtask is to test whether a
model can distinguish between natural language statements that make sense and
those that do not make sense. We compare the performance of several language
models and fine-tuned classifiers. Then, we propose a method inspired by
question/answering tasks to treat a classification problem as a multiple choice
question task to boost the performance of our experimental results (96.06%),
which is significantly better than the baseline. For the second subtask, which
is to select the reason why a statement does not make sense, we stand within
the first six teams (93.7%) among 27 participants with very competitive
results. Our result for last subtask of generating reason against the nonsense
statement shows many potentials for future researches as we applied the most
powerful generative model of language (GPT-2) with 6.1732 BLEU score among
first four teams.

本文介绍了 SemEval-2020 任务 4：Commonsense Validation and Explanation（ComVE）挑战中的常识推理任务，探讨了几种针对该任务的深度学习方法，并使用多选题式的分类方法来提高模型准确率。研究结果表明，我们的模型在三个子任务中表现良好，并在第二个子任务中获得了很高的排名。同时，我们使用了一种强大的生成模型进行了最后一个子任务的研究，现在还有许多相关的潜在研究方向。