This paper describes our participation in SemEval 2020 Task 7 on assessment of humor in edited news headlines, which includes two subtasks, estimating the humor of micro-editd news headlines (subtask A) and predicting the more humorous of the two edited headlines (subtask B). To address these tasks, we propose two systems. The first system adopts a regression-based fine-tuned single-sequence bidirectional encoder representations from transformers (BERT) model with easy data augmentation (EDA), called “BERT+EDA”. The second system adopts a hybrid of a regression-based fine-tuned sequence-pair BERT model and a combined Naive Bayes and support vector machine (SVM) model estimated on term frequency–inverse document frequency (TFIDF) features, called “BERT+NB-SVM”. In this case, no additional training datasets were used, and the BERT+NB-SVM model outperformed BERT+EDA. The official root-mean-square deviation (RMSE) score for subtask A is 0.57369 and ranks 31st out of 48, whereas the best RMSE of BERT+NB-SVM is 0.52429, ranking 7th. For subtask B, we simply use a sequence-pair BERT model, the official accuracy of which is 0.53196 and ranks 25th out of 32.