CSECU-DSG at SemEval-2021 Task 7: Detecting and Rating Humor and Offense Employing Transformers

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1204–1208 Bangkok, Thailand (online), August 5–6, 2021. ©2021 Association for Computational Linguistics 1204 CSECU-DSG at SemEval-2021 Task 7: Detecting and Rating Humor and Offense Employing Transformers Afrin Sultana, Nabila Ayman, and Abu Nowshed Chy Department of Computer Science and Engineering University of Chittagong, Chattogram-4331, Bangladesh {afrin.sultana.cu, nabila.ayman.cu}@gmail.com, and nowshed@cu.ac.bd Abstract With the emerging trends of using online plat- forms, peoples are increasingly interested in express their opinion through humorous texts. Identifying and rating humorous texts poses unique challenges to NLP due to subjective phe- nomena i.e. humor may vary to gender, profes- sion, age, and classes of people. Besides, words with multiple senses, cultural domain, and prag- matic competence also need to be considered. A humorous text may be offensive to others. To address these challenges SemEval-2021 in- troduced a HaHackathon task focusing on de- tecting and rating humorous and offensive texts. This paper describes our participation in this task. We employed a stacked embedding and fine-tuned transformer models based classifica- tion and regression approach from the features from GPT-2 medium, BERT, and RoBERTa transformer models. Besides, we utilized the fine-tuned BERT and RoBERTa models to ex- amine the performances. Our method achieved competitive performances in this task. Keywords: humor and offense rating, transform- ers, BERT, RoBERTa, stacked embedding. 1 Introduction The exponential evolution of technologies and social platforms increases the growth of user- generated content. Therefore, detecting specific information from a pile of web data is ubiquitous. Humor, like most figurative language, poses in- teresting linguistic challenges to NLP, due to its emphasis on multiple word senses, cultural knowl- edge, and pragmatic competence. The automated humor detection and rating is one of the challeng- ing and promising tasks for their significance in the field of opinion mining, sentiment analysis, and emotion intelligence domain. The first two authors have equal contributions. Numerous works have been done on humorous text identification task. (Weller and Seppi, 2019) proposed pre-trained BERT architecture to iden- tify humorous jokes from Reddit dataset, puns, and short jokes dataset. (Khatri and Pranav, 2020) used BERT and GloVe embeddings with linear support vector classifier (SVC), naive Bayes, and random forest for predicting the final label to detect sarcasm in tweets. (Badlani et al., 2019) extracted features pertaining to sarcasm, humor, hate speech, as well as sentiment and ensemble them for sentiment clas- sification. In (Liang et al., 2020), the pre-trained BERT is adapted to the distantly supervised NER (named entity recognition) task with early stopping. Some evaluation campaigns related to automatic humor detection also has been performed. (Swamy et al., 2020) includes a logistic regression baseline, a BiLSTM + attention-based learner, and a transfer learning approach with BERT to tackle the Internet humor at SemEval-2020 Task 8. However, most of the existing work addressed the humorous text identification problem as a bi- nary classification task. But the humorous rating of a text can be varied at different scales based on its contents. Moreover, a humorous text may …

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