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

Anh Gia-Tuan Nguyen; Kiet Van Nguyen; Ngan Luu-Thuy Nguyen; Tin Van Huynh; Vu Duc Nguyen

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

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arxiv 1911.03644 v3 pith:7VOIECJB submitted 2019-11-09 cs.CL

Tin Van Huynh , Vu Duc Nguyen , Kiet Van Nguyen , Ngan Luu-Thuy Nguyen , Anh Gia-Tuan Nguyen This is my paper · ORCID

classification cs.CL

keywords detectionhatespeechtaskbi-gru-lstm-cnnsocialbecomeclassifier

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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.

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Transformer-Based Contextualized Language Models Joint with Neural Networks for Natural Language Inference in Vietnamese
cs.CL 2024-11 reject novelty 3.0 of 10

A joint model of XLM-R embeddings and a CNN classifier reaches 82.78% F1 on Vietnamese NLI, but the claimed consistent superiority over fine-tuned baselines is not supported by the paper's own results.

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