MLEngineer at SemEval-2020 Task 7: BERT-Flair Based Humor Detection Model (BFHumor)

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1041–1048 Barcelona, Spain (Online), December 12, 2020. 1041 MLEngineer at SemEval-2020 Task 7: BERT-Flair based Humor Detection Model (BFHumor) Farah Shatnawi ffshatnawi16@cit.just.edu.jo Malak Abdullah mabdullah@just.edu.jo Department of Computer Science Jordan University of Science and Technology Irbid, Jordan Mahmoud Hammad m-hammad@just.edu.jo Abstract Task 7, Assessing the Funniness of Edited News Headlines, in the International Workshop SemEval2020 introduces two sub-tasks to predict the funniness values of edited news headlines from the Reddit website. This paper proposes the BFHumor model of the MLEngineer team that participates in both sub-tasks in this competition. The BFHumor’s model is defined as a BERT-Flair based humor detection model that is a combination of different pre-trained models with various Natural Language Processing (NLP) techniques. The Bidirectional Encoder Representations from Transformers (BERT) regressor is considered the primary pre-trained model in our approach, whereas Flair is the main NLP library. It is worth mentioning that the BFHumor model has been ranked 4th in sub-task1 with a root mean square error (RMSE) value of 0.51966, and it is 0.02 away from the first ranked model. Also, the team is ranked 12th in the sub-task2 with an accuracy of 0.62291, which is 0.05 away from the top-ranked model. Our results indicate that the BFHumor model is one of the top models for detecting humor in the text. 1 Introduction Humor is a distinctive quality of humans, which is considered a figurative language; where the person can express his feelings through social media once entertainment media have existed or if he faces a funny event. Researchers found that humor leads to enhance the human’s health and mood (Hern´andez-Far´ıas et al., 2015; Mao and Liu, 2019; Reyes et al., 2012; Yan and Pedersen, 2017). In the last decades, the world has witnessed a rapid move in Artificial Intelligent (AI) field toward simulating human intelligence in machines or computer systems (Wen et al., 2019; Raja and S, 2019). One of the main branches of AI is Natural Language Processing (NLP), where the computer learns how to understand human languages regardless the diversity and complexity of human languages that are considered open challenges for NLP systems and communities (Abdullah and Shaikh, 2018; Zhou et al., 2020). Knowing that humor has many facets and can be produced through words (texts), gestures (vision), and prosodic cues (acoustic) (Hasan et al., 2019; Abdullah et al., 2018), humor detection is considered a multimodal language. The reasons behind the difficulty of detecting humor are due to (1) the nature of the context, emotion, and rhythm, which contains a large degree of complexity for the machine to understand the level of the humor. (2) the cultural differences, knowing that humor is universal, the different cultures see the humor in various ways (Martin et al., 1993). Detecting humor from social media sites or TV dialogues using a set of algorithms had been studied thoroughly in the NLP field (Glazkova et al., 2019; Bertero and Fung, 2016). In this paper, we detect the humor …

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