DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection
Year: 2020
Type: conference-paper
Abstract: This paper describes our team work and submission for the SemEval 2020 (Sub-Task A) "Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media".Our two baseline models were based on different levels of representation: character vs. word level.In word level based representation we implemented a convolutional neural network model and a bi-directional GRU model.In character level based representation we implemented a hyper CNN and LSTM model.All of these models have been further augmented with attention layers for a better performance on our task.We also experimented with three types of static word embeddings: word2vec, FastText, and Glove, in addition to emoji embeddings, and compared the performance of the different deep learning models on the dataset provided by this task.The bi-directional GRU model with attention has achieved the highest score (0.85% F1 score) among all other models.
Institutions: Al-Sham Private University, Damascus University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3117825923