Work

KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT

Year: 2020

Type: conference-paper

Abstract: This research presents our team KEIS@JUST participation at SemEval-2020 Task 12 which represents shared task on multilingual offensive language.We participated in all the provided languages for all subtasks except sub-task-A for the English language.Two main approaches have been developed the first is performed to tackle both languages Arabic and English, a weighted ensemble consists of Bi-GRU and CNN followed by Gaussian noise and global pooling layer multiplied by weights to improve the overall performance.The second is performed for other languages, a transfer learning from BERT beside the recurrent neural networks such as Bi-LSTM and Bi-GRU followed by a global average pooling layer.Word embedding and contextual embedding have been used as features, moreover, data augmentation has been used only for the Arabic language .

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2020.semeval-1.269

Authors: Saja Tawalbeh, Mahmoud Hammad, Mohammad AL-Smadi

Institutions: Jordan University of Science and Technology, University of Jordan

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Jordan University of Science and Technology

Award: G2494652760