MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization
Canwen Xu, Jiaxin Pei, Hongtao Wu, Yiyu Liu, Chenliang Li · 2020
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