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Goal selection and feedback for solving math word problems.

Daijun He1, Jing Xiao1

  • 1School of Computer Science, South China Normal University, No. 55 Zhongshan West Road, Guangzhou, 510631 Guangdong China.

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|November 7, 2022
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Summary
This summary is machine-generated.

This study introduces a novel Goal Selection and Feedback (GSF) module to improve AI-powered math word problem (MWP) solving. The new approach enhances performance, particularly for complex problems with long mathematical expressions.

Keywords:
AttentionDecodingGoal selection and feedbackMWPsNLP

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Educational Technology

Background:

  • Automated solving of Math Word Problems (MWPs) is crucial for AI-tutoring in online education.
  • Existing State-Of-The-Art (SOTA) neural models often use Goal-driven Tree-structured Solvers (GTS), which struggle with long expressions due to limitations in accessing all generated node information per decoding step.

Purpose of the Study:

  • To address the limitations of current models in solving complex MWPs with long mathematical expressions.
  • To propose a novel decoding module that enhances information flow and representation during the problem-solving process.

Main Methods:

  • Introduction of a Goal Selection and Feedback (GSF) decoding module, incorporating goal feedback and attention-based goal selection.
  • Integration of a Multilayer Fusion Network (MFN) for improved hidden state representation.
  • Utilizing the ELECTRA language model in conjunction with the novel decoder.

Main Results:

  • The proposed model, combining ELECTRA and the GSF module, outperforms SOTA baselines on Math23k, Ape-clean, and MAWPS datasets.
  • Significant improvements were observed specifically for MWPs involving complex samples and long mathematical expressions.
  • Ablation and case studies confirmed the effectiveness of the GSF module and MFN in handling long expressions.

Conclusions:

  • The novel GSF decoding module and MFN effectively enhance the performance of AI models for solving Math Word Problems.
  • The proposed approach demonstrates superior capabilities in handling complex MWPs with long mathematical expressions, advancing AI-tutoring systems.