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

TL;DR

This paper presents a deep learning approach using a Bi-GRU-LSTM-CNN classifier for hate speech detection on Vietnamese social media, achieving a competitive F1-score in a shared task.

Contribution

It introduces a novel combination of Bi-GRU, LSTM, and CNN for hate speech detection in Vietnamese social media text.

Findings

Achieved 70.576% F1-score on the test set.

Ranked 5th in the shared task.

Demonstrated effectiveness of deep learning in Vietnamese hate speech detection.

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.