Reasoning arithmetic word problems entailing implicit relations based on the chain-of-thought model

Hao Meng, Lin Yue, Geng Sun, Jun Shen

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Solving arithmetic word problems (AWPs) that involve deep implicit relations can be quite challenging. However, the paper proposed two approaches to tackle this issue. The first approach used the modifier-to-matrix (MTM) model to extract noun modification components from the problem text. Specifically, a missing entity recovery (MER) model translated explicit expressions into a node dependency graph (NDG). The nodes on the graph then recursively acquired connections from the knowledge base through the MER model until the goal was achieved with a known quantity. The solving engine then selected the appropriate knowledge as the prompt. The second approach proposed in the paper was a comprehensive one that combined explicit and implicit knowledge to enhance reasoning abilities. The experimental results of the dataset demonstrate that the proposed algorithm is superior to the baseline algorithms in solving AWPs that require deep implicit relations.
Original languageEnglish
Pages (from-to)251-262
Number of pages12
JournalSTEM Education
Issue number4
Publication statusPublished - 2023

Bibliographical note

© 2023 the Author(s), licensee AIMS Press. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • arithmetic word problem (AWP)
  • implicit relation
  • chain-of-thought
  • missing entity recovery (MER)
  • prompt


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