  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.
