Hate Speech Detection on Vietnamese Social Media Text using the Bi-GRU-LSTM-CNN Model

Abstract

In recent years, Hate Speech Detection has become one of the interesting fields in natural language processing or computational linguistics. In this paper, we present the description of our system to solve this problem at the VLSP shared task 2019: Hate Speech Detection on Social Networks with the corpus which contains 20,345 human-labeled comments/posts for training and 5,086 for public-testing. We implement a deep learning method based on the Bi-GRU-LSTM-CNN classifier into this task. Our result in this task is 70.576% of F1-score, ranking the 5th of performance on public-test set.

arXiv commentTechnical Report, VLSP Workshop 2019

References in corpus (4)

• Efficient Estimation of Word Representations in Vector Space
• Recurrent Neural Network for Text Classification with Multi-Task Learning
• Enriching Word Vectors with Subword Information
• Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter

Cited by in corpus (4)

• Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art
• Comparison Between Traditional Machine Learning Models And Neural Network Models For Vietnamese Hate Speech Detection
• Predicting Job Titles from Job Descriptions with Multi-label Text Classification
• Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese