Solving Math Word Problem with Problem Type Classification

Jie Yao, Zihao Zhou, Qiufeng Wang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)

Abstract

Math word problems (MWPs) require analyzing text descriptions and generating mathematical equations to derive solutions. Existing works focus on solving MWPs with two types of solvers: tree-based solver and large language model (LLM) solver. However, these approaches always solve MWPs by a single solver, which will bring the following problems: (1) Single type of solver is hard to solve all types of MWPs well. (2) A single solver will result in poor performance due to over-fitting. To address these challenges, this paper utilizes multiple ensemble approaches to improve MWP-solving ability. Firstly, We propose a problem type classifier that combines the strengths of the tree-based solver and the LLM solver. This ensemble approach leverages their respective advantages and broadens the range of MWPs that can be solved. Furthermore, we also apply ensemble techniques to both tree-based solver and LLM solver to improve their performance. For the tree-based solver, we propose an ensemble learning framework based on ten-fold cross-validation and voting mechanism. In the LLM solver, we adopt self-consistency (SC) method to improve answer selection. Experimental results demonstrate the effectiveness of these ensemble approaches in enhancing MWP-solving ability. The comprehensive evaluation showcases improved performance, validating the advantages of our proposed approach. Our code is available at this url: https://github.com/zhouzihao501/NLPCC2023-Shared-Task3-ChineseMWP.

Original languageEnglish
Title of host publicationNatural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings
EditorsFei Liu, Nan Duan, Qingting Xu, Yu Hong
PublisherSpringer Science and Business Media Deutschland GmbH
Pages123-134
Number of pages12
ISBN (Print)9783031446986
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023 - Foshan, China
Duration: 12 Oct 202315 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14304 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023
Country/TerritoryChina
CityFoshan
Period12/10/2315/10/23

Keywords

  • Bert2Tree
  • Ensemble Learning
  • Large Language Model
  • Math Word Problem

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