MLEngineer at SemEval-2020 Task 7: BERT-Flair Based Humor Detection Model (BFHumor)
Farah Shatnawi, Malak Abdullah, Mahmoud Hammad · 2020
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 4 th 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 12 th 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.