Dual-Class Prompt Generation: Enhancing Indonesian Gender-Based Hate Speech Detection through Data Augmentation

• URL: http://arxiv.org/abs/2503.04279v1
• Date: Thu, 06 Mar 2025 10:07:51 GMT
• Title: Dual-Class Prompt Generation: Enhancing Indonesian Gender-Based Hate Speech Detection through Data Augmentation
• Authors: Muhammad Amien Ibrahim, Faisal, Tora Sangputra Yopie Winarto, Zefanya Delvin Sulistiya,
• Abstract summary: Detecting gender-based hate speech in Indonesian social media remains challenging due to limited labeled datasets.<n>We evaluate backtranslation, single-class prompt generation, and our proposed dual-class prompt generation.<n>Our findings suggest that incorporating examples from both classes helps language models generate more diverse yet representative samples.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting gender-based hate speech in Indonesian social media remains challenging due to limited labeled datasets. While binary hate speech classification has advanced, a more granular category like gender-targeted hate speech is understudied because of class imbalance issues. This paper addresses this gap by comparing three data augmentation techniques for Indonesian gender-based hate speech detection. We evaluate backtranslation, single-class prompt generation (using only hate speech examples), and our proposed dual-class prompt generation (using both hate speech and non-hate speech examples). Experiments show all augmentation methods improve classification performance, with our dual-class approach achieving the best results (88.5% accuracy, 88.1% F1-score using Random Forest). Semantic similarity analysis reveals dual-class prompt generation produces the most novel content, while T-SNE visualizations confirm these samples occupy distinct feature space regions while maintaining class characteristics. Our findings suggest that incorporating examples from both classes helps language models generate more diverse yet representative samples, effectively addressing limited data challenges in specialized hate speech detection.

Related papers

• Hierarchical Sentiment Analysis Framework for Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy [0.0]
  We propose a new multitask model integrated with shared emotional representations to detect hate speech across the English language. We conclude that utilizing sentiment analysis and a Transformer-based trained model considerably improves hate speech detection across multiple datasets.
  arXiv  Detail & Related papers  (2024-11-03T04:11:33Z)
• Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language [6.200058263544999]
  This study focuses on detecting bilingual hate speech in YouTube comments. We include factors such as content similarity, definition similarity, and common hate words to measure the impact of datasets on performance. The best performance was obtained by combining datasets from YouTube comments, Twitter, and Gab with an F1-score of 0.74 and 0.68 for English and German YouTube comments.
  arXiv  Detail & Related papers  (2024-10-02T10:22:53Z)
• Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets [0.27309692684728604]
  We propose a novel approach that leverages ensemble learning and semi-supervised learning based on previously manually labeled. We conducted experiments on a benchmark dataset by classifying Arabic tweets into 5 distinct classes: non-hate, general hate, racial, religious, or sexism.
  arXiv  Detail & Related papers  (2024-07-02T17:26:26Z)
• TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation [97.54885207518946]
  We introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion. We propose two separated encoders to preserve the speaker's voice characteristics and isochrony from the source speech during the translation process. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model.
  arXiv  Detail & Related papers  (2024-05-28T04:11:37Z)
• Improved two-stage hate speech classification for twitter based on Deep Neural Networks [0.0]
  Hate speech is a form of online harassment that involves the use of abusive language. The model we propose in this work is an extension of an existing approach based on LSTM neural network architectures. Our study includes a performance comparison of several proposed alternative methods for the second stage evaluated on a public corpus of 16k tweets.
  arXiv  Detail & Related papers  (2022-06-08T20:57:41Z)
• TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation [61.564874831498145]
  TranSpeech is a speech-to-speech translation model with bilateral perturbation. We establish a non-autoregressive S2ST technique, which repeatedly masks and predicts unit choices. TranSpeech shows a significant improvement in inference latency, enabling speedup up to 21.4x than autoregressive technique.
  arXiv  Detail & Related papers  (2022-05-25T06:34:14Z)
• Self-Supervised Speech Representation Learning: A Review [105.1545308184483]
  Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. This review presents approaches for self-supervised speech representation learning and their connection to other research areas.
  arXiv  Detail & Related papers  (2022-05-21T16:52:57Z)
• Deep Learning for Hate Speech Detection: A Comparative Study [54.42226495344908]
  We present here a large-scale empirical comparison of deep and shallow hate-speech detection methods. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. In doing so we aim to provide guidance as to the use of hate-speech detection in practice, quantify the state-of-the-art, and identify future research directions.
  arXiv  Detail & Related papers  (2022-02-19T03:48:20Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Statistical Analysis of Perspective Scores on Hate Speech Detection [7.447951461558536]
  State-of-the-art hate speech classifiers are efficient only when tested on the data with the same feature distribution as training data. In such a diverse data distribution relying on low level features is the main cause of deficiency due to natural bias in data. We show that, different hate speech datasets are very similar when it comes to extract their Perspective Scores.
  arXiv  Detail & Related papers  (2021-06-22T17:17:35Z)
• Unsupervised Cross-lingual Representation Learning for Speech Recognition [63.85924123692923]
  XLSR learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations. Experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining.
  arXiv  Detail & Related papers  (2020-06-24T18:25:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.