Deep learning techniques for math word problems

Deep learning techniques for math word problems

This paper presents a comprehensive investigation into Curriculum Learning (CL) applied to Math Word Problem (MWP) solving, examining its efficacy across a spectrum of scales and difficulty levels. Our study encompasses extensive experiments conducted on both small-scale and large-scale language...

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Main Author: Vu Duc Anh
Other Authors: Luu Anh Tuan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Online Access:https://hdl.handle.net/10356/178278
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1782782024-06-14T15:37:42Z Deep learning techniques for math word problems Vu Duc Anh Luu Anh Tuan School of Computer Science and Engineering anhtuan.luu@ntu.edu.sg Computer and Information Science This paper presents a comprehensive investigation into Curriculum Learning (CL) applied to Math Word Problem (MWP) solving, examining its efficacy across a spectrum of scales and difficulty levels. Our study encompasses extensive experiments conducted on both small-scale and large-scale language models across three distinct MWP datasets, featuring problems of varying difficulty ranges. Through rigorous evaluation, we find that curriculum learning yield better performance than traditional training in MWP solving tasks. We also find the potential of anti-curriculum learning in solving hard mathematical questions. Additionally, we offer an in-depth analysis of the mechanisms and effects of curriculum learning and its variations in MWP solving Bachelor's degree 2024-06-11T12:33:45Z 2024-06-11T12:33:45Z 2024 Final Year Project (FYP) Vu Duc Anh (2024). Deep learning techniques for math word problems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/178278 https://hdl.handle.net/10356/178278 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
spellingShingle Computer and Information Science
Vu Duc Anh
Deep learning techniques for math word problems
description This paper presents a comprehensive investigation into Curriculum Learning (CL) applied to Math Word Problem (MWP) solving, examining its efficacy across a spectrum of scales and difficulty levels. Our study encompasses extensive experiments conducted on both small-scale and large-scale language models across three distinct MWP datasets, featuring problems of varying difficulty ranges. Through rigorous evaluation, we find that curriculum learning yield better performance than traditional training in MWP solving tasks. We also find the potential of anti-curriculum learning in solving hard mathematical questions. Additionally, we offer an in-depth analysis of the mechanisms and effects of curriculum learning and its variations in MWP solving
author2 Luu Anh Tuan
author_facet Luu Anh Tuan
Vu Duc Anh
format Final Year Project
author Vu Duc Anh
author_sort Vu Duc Anh
title Deep learning techniques for math word problems
title_short Deep learning techniques for math word problems
title_full Deep learning techniques for math word problems
title_fullStr Deep learning techniques for math word problems
title_full_unstemmed Deep learning techniques for math word problems
title_sort deep learning techniques for math word problems
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/178278
_version_ 1806059775028363264

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