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. |
| Outcome: | The proposed method outperforms existing models on eight languages and is highly data-efficient. |
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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. |
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
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Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection (2021.acl-short)
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| Challenge: | Existing approaches to detect hate speech are expensive and time-consuming . a new approach allows for flexible learning of neighborhood information . |
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Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models (2022.findings-aacl)
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| Challenge: | Zero-shot cross-lingual transfer learning has been shown to be challenging for tasks involving a lot of linguistic specificities or when a cultural gap is present between languages, such as hate speech detection. |
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| Challenge: | Hate speech detection depends on the availability of variable labeled data. |
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A Neighborhood Framework for Resource-Lean Content Flagging (2022.tacl-1)
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Sheikh Muhammad Sarwar, Dimitrina Zlatkova, Momchil Hardalov, Yoan Dinkov, Isabelle Augenstein, Preslav Nakov
| Challenge: | Existing approaches to cross-lingual content flagging with limited target language data are lacking in many languages. |
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