  In this paper, we present language model system submitted to SemEval-2020
Task 4 competition: "Commonsense Validation and Explanation". We participate in
two subtasks for subtask A: validation and subtask B: Explanation. We
implemented with transfer learning using pretrained language models (BERT,
XLNet, RoBERTa, and ALBERT) and fine-tune them on this task. Then we compared
their characteristics in this task to help future researchers understand and
use these models more properly. The ensembled model better solves this problem,
making the model's accuracy reached 95.9% on subtask A, which just worse than
human's by only 3% accuracy.
