Cyberbullying has become a big issue with the popularity of different social media networks and online communication apps. While plenty of research is going on to develop better models for cyberbullying detection in monolingual language, there is very little research on the code-mixed languages and explainability aspect of cyberbullying. Recent laws like "right to explanations" of General Data Protection Regulation, have spurred research in developing interpretable models rather than focusing on performance. Motivated by this we develop the first interpretable multi-task model called {\em mExCB} for automatic cyberbullying detection from code-mixed languages which can simultaneously solve several tasks, cyberbullying detection, explanation/rationale identification, target group detection and sentiment analysis. We have introduced {\em BullyExplain}, the first benchmark dataset for explainable cyberbullying detection in code-mixed language. Each post in {\em BullyExplain} dataset is annotated with four labels, i.e., {\em bully label, sentiment label, target and rationales (explainability)}, i.e., which phrases are being responsible for annotating the post as a bully. The proposed multitask framework (mExCB) based on CNN and GRU with word and sub-sentence (SS) level attention is able to outperform several baselines and state of the art models when applied on {\em BullyExplain} dataset.

翻译：网络霸凌在各类社交媒体平台和在线通讯应用普及的背景下，已演变成为一个重大问题。尽管已有大量研究致力于开发针对单语语言的网络霸凌检测模型，但在代码混合语言领域以及网络霸凌的可解释性方面，相关研究仍然十分匮乏。近年来，诸如《通用数据保护条例》中“解释权”等法规，推动了可解释模型的研究发展，将重点从单纯关注性能转向构建兼具解释性的模型。受此启发，我们开发了首个可解释的多任务模型——**mExCB**，该模型能够从代码混合语言中自动检测网络霸凌，并可同时完成多项任务：网络霸凌检测、解释/依据识别、目标群体检测以及情感分析。我们引入了**BullyExplain**，这是首个面向代码混合语言可解释网络霸凌检测的基准数据集。在**BullyExplain**数据集中，每一条帖子都标注了四个标签，即**霸凌标签、情感标签、目标对象和依据（可解释性）**，其中依据标注了导致帖子被判定为霸凌的具体短语。基于CNN和GRU，并结合词级与子句级注意机制所提出的多任务框架（mExCB），在**BullyExplain**数据集上能够超越多个基线模型及当前最先进的模型。