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System Demo: Tool and Infrastructure for Offensive Language Error Analysis (OLEA) in English

arXiv 2210.16398 ↗First posted 28 Oct 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

The automatic detection of offensive language is a pressing societal need. Many systems perform well on explicit offensive language but struggle to detect more complex, nuanced, or implicit cases of offensive and hateful language. OLEA is an open-source Python library that provides easy-to-use tools for error analysis in the context of detecting offensive language in English. OLEA also provides an infrastructure for re-distribution of new datasets and analysis methods requiring very little coding.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 28 Oct 2022.

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References · 10

  1. 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 417
  2. 2020HateXplain: A Benchmark Dataset for Explainable Hate Speech Detectioncited by 407
  3. 2019Predicting the Type and Target of Offensive Posts in Social Mediacited by 271
  4. 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
  5. 2021Latent Hatred: A Benchmark for Understanding Implicit Hate Speechcited by 217
  6. 2020HateCheck: Functional Tests for Hate Speech Detection Modelscited by 216
  7. 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 207
  8. 2016Demographic Dialectal Variation in Social Media: A Case Study of African-American Englishcited by 196
  9. 2017Understanding Abuse: A Typology of Abusive Language Detection Subtaskscited by 116
  10. 2020Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learningcited by 68

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