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Deep Learning Models for Multilingual Hate Speech Detection

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arxiv 2004.06465 v3 pith:AS5SR7LW submitted 2020-04-14 cs.SI cs.CL

Sai Saketh Aluru , Binny Mathew , Punyajoy Saha , Animesh Mukherjee This is my paper · ORCID
classification cs.SIcs.CL
keywords hatemodelsspeechdetectionlanguagesmultilingualgoodresource
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Hate speech detection is a challenging problem with most of the datasets available in only one language: English. In this paper, we conduct a large scale analysis of multilingual hate speech in 9 languages from 16 different sources. We observe that in low resource setting, simple models such as LASER embedding with logistic regression performs the best, while in high resource setting BERT based models perform better. In case of zero-shot classification, languages such as Italian and Portuguese achieve good results. Our proposed framework could be used as an efficient solution for low-resource languages. These models could also act as good baselines for future multilingual hate speech detection tasks. We have made our code and experimental settings public for other researchers at https://github.com/punyajoy/DE-LIMIT.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hate Speech According to the Law: An Analysis for Effective Detection

    cs.CL 2024-12 conditional novelty 6.0 of 10

    A 100-instance expert-annotated dataset of prosecutable hate speech under Greek, Italian, and UK law is used to benchmark BERT models and LLMs, with legal annotations helping performance and LLM-generated silver data ...

  2. ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data?

    cs.CL 2024-11 conditional novelty 4.0 of 10

    Fine-tuned open-source LLMs, especially Mistral, generate synthetic toxic data that improves downstream hate speech detection classifiers, approaching GPT-4-level performance.

  3. HateGPT: Unleashing GPT-3.5 Turbo to Combat Hate Speech on X

    cs.CL 2024-11 conditional novelty 2.0 of 10

    Zero-shot GPT-3.5 Turbo prompting achieves macro-F1 0.756 on HASOC 2024 English hate speech classification, ranking 5th.

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