mlsanityData to 7 Oct 2026

"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition

arXiv 2402.03221 ↗First posted 5 Feb 2024cs.CLcited by 7

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Abstract (arXiv)

The propagation of offensive content through social media channels has garnered attention of the research community. Multiple works have proposed various semantically related yet subtle distinct categories of offensive speech. In this work, we explore meta-earning approaches to leverage the diversity of offensive speech corpora to enhance their reliable and efficient detection. We propose a joint embedding architecture that incorporates the input's label and definition for classification via Prototypical Network. Our model achieves at least 75% of the maximal F1-score while using less than 10% of the available training data across 4 datasets. Our experimental findings also provide a case study of training strategies valuable to combat resource scarcity.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 32 of its 55 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

32 of 55 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2024Untangling Hate Speech Definitions: A Semantic Componential Analysis Across Cultures and Domainscited by 5
  2. 2024HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Modelscited by 4
  3. 2024Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodologycited by 3
  4. 2025Algorithmic Fairness in NLP: Persona-Infused LLMs for Human-Centric Hate Speech Detectioncited by 2
  5. 2025From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discoursecited by 0
  6. 2025SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Modelscited by 0
  7. 2026Improving Implicit Hate Speech Detection via a Community-Driven Multi-Agent Frameworkcited by 0