Online abusive language detection (ALD) has become a societal issue of
increasing importance in recent years. Several previous works in online ALD
focused on solving a single abusive language problem in a single domain, like
Twitter, and have not been successfully transferable to the general ALD task or
domain. In this paper, we introduce a new generic ALD framework, MACAS, which
is capable of addressing several types of ALD tasks across different domains.
Our generic framework covers multi-aspect abusive language embeddings that
represent the target and content aspects of abusive language and applies a
textual graph embedding that analyses the user's linguistic behaviour. Then, we
propose and use the cross-attention gate flow mechanism to embrace multiple
aspects of abusive language. Quantitative and qualitative evaluation results
show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD
algorithms across seven ALD datasets covering multiple aspects of abusive
language and different online community domains.

本文中我们介绍了一个名为 MACAS 的新型通用虐待性语言检测框架，该框架能够解决跨不同领域的多种虐待性语言任务，其中使用了多角度虐待性语言嵌入和文本图嵌入分析用户的语言行为，并使用了交叉注意力门机制来有效处理多方面的虐待语言，并在 7 个 ALD 数据集上进行了评估，结果表明我们的 ALD 算法在多个领域的虐待性语言问题上表现优异，可以与当前六种最先进的 ALD 算法相媲美甚至超越。