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

Source: [MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization](<https://www.sota2.com/research/paper/68fa67991e209131cea1757a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:18:12.808Z

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

Published: 2020-04-26T07:43:15.000Z

## 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. 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.12302>)

[PDF](<https://arxiv.org/pdf/2004.12302.pdf>)