GAN-BERT: Adversarial Learning for Detection of Aggressive and Violent Incidents from Social Media

Hoang Thang Ta; Abu Bakar Siddiqur Rahman; Lotfollah Najjar; Alexander Gelbukh

GAN-BERT: Adversarial Learning for Detection of Aggressive and Violent Incidents from Social Media

Hoang Thang Ta, Abu Bakar Siddiqur Rahman, Lotfollah Najjar, Alexander Gelbukh

Centro de Investigación en Computación (CIC)

Producción científica: Contribución a una revista › Artículo de la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

In this paper, we address Subtask 1 of Detection of Aggressive and Violent INCIdents from Social Media in Spanish (DA-VINCIS). We introduced our method, using text embeddings from pre-trained transformer models for the training process by GAN-BERT, an adversarial learning architecture. Finally, we obtained F1 of 74.43%, Precision of 74.08%, and Recall of 74.79% on Subtask 1.

Idioma original	Inglés
Publicación	CEUR Workshop Proceedings
Volumen	3202
Estado	Publicada - 2022
Evento	2022 Iberian Languages Evaluation Forum, IberLEF 2022 - A Coruna, Espana Duración: 20 sep. 2022 → …

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abstract = "In this paper, we address Subtask 1 of Detection of Aggressive and Violent INCIdents from Social Media in Spanish (DA-VINCIS). We introduced our method, using text embeddings from pre-trained transformer models for the training process by GAN-BERT, an adversarial learning architecture. Finally, we obtained F1 of 74.43%, Precision of 74.08%, and Recall of 74.79% on Subtask 1.",

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note = "Publisher Copyright: {\textcopyright} 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).; 2022 Iberian Languages Evaluation Forum, IberLEF 2022 ; Conference date: 20-09-2022",

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AU - Rahman, Abu Bakar Siddiqur

AU - Najjar, Lotfollah

AU - Gelbukh, Alexander

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N2 - In this paper, we address Subtask 1 of Detection of Aggressive and Violent INCIdents from Social Media in Spanish (DA-VINCIS). We introduced our method, using text embeddings from pre-trained transformer models for the training process by GAN-BERT, an adversarial learning architecture. Finally, we obtained F1 of 74.43%, Precision of 74.08%, and Recall of 74.79% on Subtask 1.

AB - In this paper, we address Subtask 1 of Detection of Aggressive and Violent INCIdents from Social Media in Spanish (DA-VINCIS). We introduced our method, using text embeddings from pre-trained transformer models for the training process by GAN-BERT, an adversarial learning architecture. Finally, we obtained F1 of 74.43%, Precision of 74.08%, and Recall of 74.79% on Subtask 1.

KW - DA-VINCIS

KW - GAN-BERT

KW - IberLEF

KW - NLP

KW - Offensive Language

KW - Text Classification

KW - Violence Detection

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JO - CEUR Workshop Proceedings

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