@inproceedings{zhang-yamana-2020-wuy,
    title = "{WUY} at {S}em{E}val-2020 Task 7: Combining {BERT} and Naive {B}ayes-{SVM} for Humor Assessment in Edited News Headlines",
    author = "Zhang, Cheng  and
      Yamana, Hayato",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.semeval-1.141/",
    doi = "10.18653/v1/2020.semeval-1.141",
    pages = "1071--1076",
    abstract = "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."
}