TY - GEN
T1 - MLEngineer at SemEval-2020 Task 7
T2 - 14th International Workshops on Semantic Evaluation, SemEval 2020, co-located with COLING 2020
AU - Shatnawi, Farah
AU - Abdullah, Malak
AU - Hammad, Mahmoud
N1 - Publisher Copyright:
© 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85113909674
U2 - 10.18653/v1/2020.semeval-1.136
DO - 10.18653/v1/2020.semeval-1.136
M3 - Conference contribution
AN - SCOPUS:85113909674
T3 - 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings
SP - 1041
EP - 1048
BT - COLING 2020 - The International Workshop on Semantic Evaluation, Proceedings of the 14th Workshop
A2 - Herbelot, Aurelie
A2 - Zhu, Xiaodan
A2 - Palmer, Alexis
A2 - Schneider, Nathan
A2 - May, Jonathan
A2 - Shutova, Ekaterina
PB - International Committee for Computational Linguistics
Y2 - 12 December 2020 through 13 December 2020
ER -