CoRAL: a Context-aware Croatian Abusive Language Dataset
원문 초록
In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying the examples or allowing the moderators to prioritize which comments to consider first. However, the concept of inappropriate content is often subjective, and such content can be conveyed in many subtle and indirect ways. In this work, we propose CoRAL -- a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context. We show experimentally that current models degrade when comments are not explicit and further degrade when language skill and context knowledge are required to interpret the comment.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings
AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages
Towards Efficient and Explainable Hate Speech Detection via Model Distillation
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning
EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian Twitter
Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online Spaces
토론
이 논문과 연결된 포럼 글이 아직 없습니다.