  Various data mining tasks have been proposed to study Community Question
Answering (CQA) platforms like Stack Overflow. The relatedness between some of
these tasks provides useful learning signals to each other via Multi-Task
Learning (MTL). However, due to the high heterogeneity of these tasks, few
existing works manage to jointly solve them in a unified framework. To tackle
this challenge, we develop a multi-relational graph based MTL model called
Heterogeneous Multi-Task Graph Isomorphism Network (HMTGIN) which efficiently
solves heterogeneous CQA tasks. In each training forward pass, HMTGIN embeds
the input CQA forum graph by an extension of Graph Isomorphism Network and skip
connections. The embeddings are then shared across all task-specific output
layers to compute respective losses. Moreover, two cross-task constraints based
on the domain knowledge about tasks' relationships are used to regularize the
joint learning. In the evaluation, the embeddings are shared among different
task-specific output layers to make corresponding predictions. To the best of
our knowledge, HMTGIN is the first MTL model capable of tackling CQA tasks from
the aspect of multi-relational graphs. To evaluate HMTGIN's effectiveness, we
build a novel large-scale multi-relational graph CQA dataset with over two
million nodes from Stack Overflow. Extensive experiments show that: $(1)$
HMTGIN is superior to all baselines on five tasks; $(2)$ The proposed MTL
strategy and cross-task constraints have substantial advantages.
