What Did You Learn To Hate? A Topic-Oriented Analysis of Generalization in Hate Speech Detection (2023.eacl-main)
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| Challenge: | Hate speech detection datasets often use different annotation guidelines, resulting in inconsistencies . authors propose a topic-oriented approach to study generalization across popular hate speech datasets . |
| Approach: | They propose a topic-oriented approach to study generalization across popular hate speech datasets . they compare Transformer-based models in capturing topic-generic and topic-specific knowledge . |
| Outcome: | The proposed approach improves the reliability of hate speech detection on social media platforms. |
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| Challenge: | Hate speech detection models struggle to generalise beyond their training data . dataset biases and the use of sentence-level labels fail to teach the underlying structure of hate speech. |
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Manuel Tonneau, Diyi Liu, Niyati Malhotra, Scott A. Hale, Samuel Fraiberger, Victor Orozco-Olvera, Paul Röttger
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| Challenge: | a recent study shows that many definitions are being used for equivalent concepts, making most datasets incompatible. |
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| Challenge: | Existing methods for hate speech detection are limited in size and lack of labeled datasets. |
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| Challenge: | Current research on hate speech analysis is oriented towards monolingual and single classification tasks. |
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
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