Math word Problem generation using transformers and reinforcement learning

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Naresh M; D. Naga Jyothi; P. Guru Prasad

Math word Problem generation using transformers and reinforcement learning

Authors: Naresh M, D. Naga Jyothi, P. Guru Prasad

Unique Paper ID: 176812
Volume: 11
Issue: 11
PageNo: 7593-7598

Keywords: Math Word Problems (MWPs) · Automated Problem Generation · Transformer Architecture · Reinforcement Learning · Natural Language Processing (NLP).

Abstract:
Manually crafting math word problems is a labour-intensive process that teachers do, and one can sense a growing need for automated systems. However, many of the present models will generate problems that are grammatically correct but semantically incoherent, not solvable, or not aligned with the educational objectives. Addressing these issues is the motivation behind our work that enhances an MWP generation model using transformer architecture and reinforcement learning. Having integrated the topic-expression transformer mechanism, our approach will be to align the problem context with appropriate mathematical operations: MWPs are generated that are linguistically sound and mathematically proper. Towards the future, we would focus on the increase of diversity and complexity of the generated problems and evaluation of model adaptability across different datasets. Finally, we shall end up with an application that is user-friendly to enable real-time generation and interaction with MWPs with improved relevance, solvability and effectiveness in the educational setting.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 7593-7598

Math word Problem generation using transformers and reinforcement learning

Available:https://ijirt.org/article?manuscript=176812

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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