Battling Hateful Content in Indic Languages HASOC '21
원문 초록
The extensive rise in consumption of online social media (OSMs) by a large number of people poses a critical problem of curbing the spread of hateful content on these platforms. With the growing usage of OSMs in multiple languages, the task of detecting and characterizing hate becomes more complex. The subtle variations of code-mixed texts along with switching scripts only add to the complexity. This paper presents a solution for the HASOC 2021 Multilingual Twitter Hate-Speech Detection challenge by team PreCog IIIT Hyderabad. We adopt a multilingual transformer based approach and describe our architecture for all 6 subtasks as part of the challenge. Out of the 6 teams that participated in all the subtasks, our submissions rank 3rd overall.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection
All You Need is "Leet": Evading Hate-speech Detection AI
Personalisation or Prejudice? Addressing Geographic Bias in Hate Speech Detection using Debias Tuning in Large Language Models
Improving Hate Speech Classification with Cross-Taxonomy Dataset Integration
Evaluation of Hate Speech Detection Using Large Language Models and Geographical Contextualization
Subjective Logic Encodings
토론
이 논문과 연결된 포럼 글이 아직 없습니다.