Solving Math Word Problem with Problem Type Classification
Jie Yao Zihao Zhou Qiufeng Wang
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.