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Math Word Problem Solver Based on Text-to-Text Transformer Model

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成果类型:
会议论文
作者:
Chuanzhi Yang;Runze Huang;Xinguo Yu;Rao Peng
作者机构:
[Xinguo Yu; Rao Peng] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
[Chuanzhi Yang; Runze Huang] School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
语种:
英文
关键词:
math word problem;problem solver;text-to-text transformer model;deep learning
年:
2021
页码:
818-822
会议名称:
2021 IEEE International Conference on Engineering, Technology & Education (TALE)
会议论文集名称:
2021 IEEE International Conference on Engineering, Technology & Education (TALE)
会议时间:
05 December 2021
会议地点:
Wuhan, Hubei Province, China
出版者:
IEEE
ISBN:
978-1-6654-3688-5
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61977029)
机构署名:
本校为第一机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
In recent years, automatic problem solving for math-ematical words has attracted increasing attention. Therefore, algorithms developed for solving mathematical word problems like humans are a key technology to facilitate the development of digital education. In this paper, a deep learning model based on a text-to-text conversion model is proposed to solve mathematical word problems. The deep learning model treats each mathematical word problem as a “text-to-text” problem, i.e. taking a text as input and producing a new text as output. However...

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