NOAHQA: Numerical reasoning with interpretable graph question answering dataset

• Main Authors: ZHANG, Qiyuan, WANG, Lei, YU, Sicheng, WANG, Shuohang, WANG, Yang, JIANG, Jing, LIM, Ee-peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/7153; https://ink.library.smu.edu.sg/context/sis_research/article/8156/viewcontent/2109.10604.pdf
• Institution: Singapore Management University

While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects. First, we lack QA datasets covering complex questions that involve answers as well as the reasonin...