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This paper presents our systems for the three Subtasks of SemEval Task4:Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithmsused to learn our models and the process of tuning the algorithms and selectingthe best model. Inspired by the similarity of the ReCAM task and the languagepre-training, we propose a simple yet effective technology, namely, negativeaugmentation with language model. Evaluation results demonstrate theeffectiveness of our proposed approach. Our models achieve the 4th rank on bothofficial test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and anaccuracy of 92.8%, respectively. We further conduct comprehensive modelanalysis and observe interesting error cases, which may promote futureresearches.

ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning