Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages (2022.emnlp-main)
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| Challenge: | Hate speech datasets focus on English-language content, hindering effective models . annotating hateful content is expensive, time-consuming and potentially harmful to annotators. |
| Approach: | They propose to use ISO 639-1 codes to fine-tune models on one source language and apply them to another language. |
| Outcome: | The proposed approach performs well on some tasks, but fails on many others. |
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
| Approach: | They propose an input-level data augmentation technique EasyMix to improve hate speech detection in english and multilingual datasets. |
| Outcome: | The proposed method improves the performance across english and multilingual datasets by 1% and 2-8%. |
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data (2025.emnlp-main)
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| Challenge: | Existing methods for detecting hate speech data are expensive and time-consuming . labeled data is expensive and difficult to collect, especially for low-resource languages . |
| Approach: | They propose a method that leverages nearest-neighbor retrieval to augment minimal labeled data in target language. |
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Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech (2021.findings-emnlp)
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| Challenge: | Existing methods for hate speech detection are limited in size and lack of labeled datasets. |
| Approach: | They employ pretrained language models to generate large amounts of hate speech sequences from available labeled examples. |
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Delving into Qualitative Implications of Synthetic Data for Hate Speech Detection (2024.emnlp-main)
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| Challenge: | Recent work on synthetic data for training models for NLP tasks reports mixed results on subjective tasks such as hate speech detection. |
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Word-Level Detection of Code-Mixed Hate Speech with Multilingual Domain Transfer (2025.findings-acl)
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| Challenge: | a growing problem in language detection tasks is code-mixing, a combination of more than one language . lack of available datasets for code-mixing causes the problem . authors propose a multilingual approach to code-matching . |
| Approach: | They propose to use an annotated hate speech dataset to detect code-mixing in profane language . they propose to apply bilingual fine-tuned models to code-mixed hate speech in german rap lyrics . |
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Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection (2021.acl-short)
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| Challenge: | a lack of labeled, non-English resources for hate speech detection limits research on hate speech . a recent study shows that zero-shot, cross-lingual learning models cannot be used as they are . lack of consistency limits research, and lack of models for non-english languages limits learning . |
| Approach: | They propose a zero-shot, cross-lingual transfer learning framework for hate speech detection . they use benchmark data sets in English, Italian, and Spanish to detect hate speech . |
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Multilingual and Multi-Aspect Hate Speech Analysis (D19-1)
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| Challenge: | Current research on hate speech analysis is oriented towards monolingual and single classification tasks. |
| Approach: | They propose to use a multilingual multi-aspect hate speech analysis dataset to test current methods . they evaluate the dataset in various classification settings and discuss how to leverage annotations . |
| Outcome: | The proposed dataset can be used to improve hate speech detection and classification in general. |
Probing LLMs for hate speech detection: strengths and vulnerabilities (2023.findings-emnlp)
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| Challenge: | Recent efforts to detect hateful or toxic language using large language models have not used explanation, additional context and victim community information in the detection process. |
| Approach: | They use different prompt variations, input information and victim community information to evaluate large language models in zero shot setting without adding any in-context examples. |
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Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020.acl-main)
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| Challenge: | Hate Speech (HS) is a pervasive issue that spreads quickly and widely . research has focused on avoiding undesired effects that come with content moderation . |
| Approach: | They propose to use large scale unsupervised language models to generate responses to hate effectively using large scale models. |
| Outcome: | The proposed methods lack quality data and produce generic/repetitive responses. |
HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models (2024.findings-emnlp)
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| Challenge: | Social media has amplified the propagation of hateful sentiments, highlighting the contested nature of "offensive content" research shows that "of offensive content" is still a contested construct due to varying definitions and labeling. |
| Approach: | They propose a dataset that features human-curated explanations for offensive content in English . they show that HateCOT pretraining improves performance of open-source LLMs . |
| Outcome: | The proposed model improves on three benchmark datasets for offensive content detection . the model improve the quality of its explanations, as confirmed by the human evaluation . |