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.
@article{arxiv.2004.12302,
title = {MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization},
author = {Canwen Xu and Jiaxin Pei and Hongtao Wu and Yiyu Liu and Chenliang Li},
journal= {arXiv preprint arXiv:2004.12302},
year = {2020}
}