  Recently, large-scale datasets have vastly facilitated the development in
nearly all domains of Natural Language Processing. However, there is currently
no cross-task dataset in NLP, which hinders the development of multi-task
learning. We propose MATINF, the first jointly labeled large-scale dataset for
classification, question answering and summarization. MATINF contains 1.07
million question-answer pairs with human-labeled categories and user-generated
question descriptions. Based on such rich information, MATINF is applicable for
three major NLP tasks, including classification, question answering, and
summarization. We benchmark existing methods and a novel multi-task baseline
over MATINF to inspire further research. Our comprehensive comparison and
experiments over MATINF and other datasets demonstrate the merits held by
MATINF.
