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A Theoretical Review on Solving Algebra Problems

arXiv 2411.00031 ↗First posted 29 Oct 2024cs.LO cs.AI cs.SCcited by 0

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Abstract (arXiv)

Solving algebra problems (APs) continues to attract significant research interest as evidenced by the large number of algorithms and theories proposed over the past decade. Despite these important research contributions, however, the body of work remains incomplete in terms of theoretical justification and scope. The current contribution intends to fill the gap by developing a review framework that aims to lay a theoretical base, create an evaluation scheme, and extend the scope of the investigation. This paper first develops the State Transform Theory (STT), which emphasizes that the problem-solving algorithms are structured according to states and transforms unlike the understanding that underlies traditional surveys which merely emphasize the progress of transforms. The STT, thus, lays the theoretical basis for a new framework for reviewing algorithms. This new construct accommodates the relation-centric algorithms for solving both word and diagrammatic algebra problems. The latter not only highlights the necessity of introducing new states but also allows revelation of contributions of individual algorithms obscured in prior reviews without this approach.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 73 references that are in the map. First posted on arXiv 29 Oct 2024.

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References · 19

  1. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 650
  2. 2016Solving General Arithmetic Word Problemscited by 360
  3. 2023Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verificationcited by 166
  4. 2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
  5. 2022Solving Math Word Problems via Cooperative Reasoning induced Language Modelscited by 77
  6. 2018Translating a Math Word Problem to an Expression Treecited by 57
  7. 2022Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extractioncited by 44
  8. 2020PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generationcited by 42
  9. 2021Neural-Symbolic Solver for Math Word Problems with Auxiliary Taskscited by 38
  10. 2017Mapping to Declarative Knowledge for Word Problem Solvingcited by 36
  11. 2021MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solvingcited by 32
  12. 2018The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solverscited by 31
7 more
  1. 2020Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problemcited by 31
  2. 2021Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problemscited by 30
  3. 2021Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problemscited by 12
  4. 2021Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solvingcited by 11
  5. 2016Equation Parsing: Mapping Sentences to Grounded Equationscited by 9
  6. 2022Generalizing Math Word Problem Solvers via Solution Diversificationcited by 7
  7. 2021Theory and Practice of Algorithm Engineeringcited by 1

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