D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation (2024.emnlp-main)
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| Challenge: | Recent studies on annotator subjectivity focus on Western contexts and only document differences across age, gender, or racial groups. |
| Approach: | They propose a large-scale cross-cultural dataset of parallel annotations for offensive language in over 4.5K English sentences annotated by a pool of more than 4k annotators from 21 countries. |
| Outcome: | The proposed dataset captures annotators’ moral values along six moral foundations: care, equality, proportionality, authority, loyalty, and purity. |
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| Challenge: | supervised learning is a key component of offensive language detection, but there is little attention given to the quality of annotated data. |
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| Challenge: | Existing datasets for hate speech detection neglect the cultural diversity within a single language. |
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The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse (2022.lrec-1)
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Ritesh Kumar, Shyam Ratan, Siddharth Singh, Enakshi Nandi, Laishram Niranjana Devi, Akash Bhagat, Yogesh Dawer, Bornini Lahiri, Akanksha Bansal, Atul Kr. Ojha
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| Challenge: | Detecting offensive content is becoming a critical task in natural language processing . but most datasets use a single binary label for hate or incivility, even though each concept is multi-faceted . a more fine-grained multi-label approach addresses conceptual and performance issues . |
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| Challenge: | Conventional approaches aggregate annotator judgements (labels), ignoring minority perspectives, and overlooking the influence of sociocultural context behind such annotations. |
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| Challenge: | a new study shows that cultural background significantly affects multimodal hate speech moderation models . a limited dataset excludes multi-modal forms of hate and excludes non-English-speaking cultures . the lowest pairwise label agreement between the USA and India is due to cultural factors . |
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Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks (2022.naacl-main)
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| Challenge: | Labelled data is the foundation of most natural language processing tasks, but there are valid beliefs about what the correct data labels should be. |
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Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets (2020.lrec-1)
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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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