WUY at SemEval-2020 Task 7: Combining BERT and Naive Bayes-SVM for Humor Assessment in Edited News Headlines

Cheng Zhang, Hayato Yamana
2020 Proceedings of the Fourteenth Workshop on Semantic Evaluation   unpublished
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),
more » ... alled "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. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http:// creativecommons.org/licenses/by/4.0/.
doi:10.18653/v1/2020.semeval-1.141 fatcat:6fwncpjlyzgc3g3jn2pnfpmzkm