MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization

Canwen Xu, Hongtao Wu, Chenliang Li, Jiaxin Pei, Yiyu Liu

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Abstract

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. MAT-INF contains 1.07 million question-answer pairs with human-labeled categories and usergenerated 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 MAT-INF. 1

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