Chinese Offensive Language Detection: Challenges and Solutions

核心概念

Developing effective systems for detecting offensive language in Chinese poses unique challenges due to cultural nuances and linguistic complexities.

摘要

The paper discusses the challenges and future directions of offensive language detection in Chinese.
It covers the importance of maintaining a respectful online environment and the need for automatic systems.
The content is structured into sections covering the abstract, introduction, background, datasets, current approaches and models, research gaps, challenges, addressing the issue, and conclusion.
Various examples and references are provided to support the discussion.

統計資料

Despite the increasing interest in detecting offensive language in Chinese, many studies fail to fully acknowledge the unique challenges posed by the language and culture.
The Chinese language's contextual-based nature, wide range of dialects, and regional variations make it difficult for automatic systems to detect offensive language accurately.
Offensive language detection systems need to encompass different modalities like text, images, videos, and audio to be effective.

引述

"Despite the increasing interest in detecting offensive language in Chinese, many studies fail to fully acknowledge the unique challenges posed by the language and culture." - Content
"The Chinese language's contextual-based nature, wide range of dialects, and regional variations make it difficult for automatic systems to detect offensive language accurately." - Content

從以下內容提煉的關鍵洞見

Chinese Offensive Language Detection

by Yunze Xiao,H... 於 arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18314.pdf

深入探究

How can cultural context be effectively incorporated into offensive language detection systems?

Incorporating cultural context into offensive language detection systems is crucial for accurately identifying and understanding offensive language in different cultural settings. One effective way to integrate cultural context is through the use of topic modeling and discourse-level analysis. By analyzing the topics being discussed and the overall discourse surrounding the text, the system can better interpret the intended meaning of the language used. Additionally, dialogue models can be employed to consider contextual cues and understand the broader conversation in which the offensive language is used. This approach helps the system differentiate between offensive and non-offensive uses of certain words or phrases based on the cultural context in which they are employed.

How can the potential ethical considerations when using user behavior encoding for offensive language detection?

When using user behavior encoding for offensive language detection, several ethical considerations must be taken into account. One major concern is the potential violation of privacy rights when incorporating user information such as past comments and number of followers into the detection process. It is essential to ensure that user data is handled securely and anonymously to protect individuals' privacy. Additionally, providing negative labels based on user behavior can lead to discrimination and bias, raising ethical concerns about the potential harm caused by the detection system. Careful consideration should be given to the ethical implications of using user behavior encoding, including the potential for negative labeling and privacy violations.

How can the detection of neologisms in Chinese be improved to address covert offensive language expressions?

Detecting neologisms in Chinese to address covert offensive language expressions requires a deep understanding of cultural references and language nuances. One approach to improving neologism detection is to develop models that capture cultural references and creative expressions in knowledge bases specifically designed for detecting covert offensive language. By incorporating these references and expressions into the detection algorithms, the system can better identify and analyze the use of alternative symbolic infrastructures in offensive language. Additionally, training models jointly on sarcasm and offensive language datasets can help recognize connections between different forms of language use, including neologisms. This approach enables the system to detect and interpret subtle and covert offensive language expressions more effectively.