MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers

• URL: http://arxiv.org/abs/2109.00799v1
• Date: Thu, 2 Sep 2021 09:18:09 GMT
• Title: MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers
• Authors: Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim
• Abstract summary: MWPToolkit is the first open-source framework for solving Math Word Problem (MWP) solvers. We implement and compare 17 MWP solvers on 4 widely-used single equation generation benchmarks and 2 multiple equations generation benchmarks.
• Score: 29.611442087779896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs. However, most existing methods are benchmarked soly on one or two datasets, varying in different configurations, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents MWPToolkit, the first open-source framework for solving MWPs. In MWPToolkit, we decompose the procedure of existing MWP solvers into multiple core components and decouple their models into highly reusable modules. We also provide a hyper-parameter search function to boost the performance. In total, we implement and compare 17 MWP solvers on 4 widely-used single equation generation benchmarks and 2 multiple equations generation benchmarks. These features enable our MWPToolkit to be suitable for researchers to reproduce advanced baseline models and develop new MWP solvers quickly. Code and documents are available at https://github.com/LYH-YF/MWPToolkit.

Related papers

• Cutting Through the Noise: Boosting LLM Performance on Math Word Problems [52.99006895757801]
  Large Language Models excel at solving math word problems, but struggle with real-world problems containing irrelevant information. We propose a prompting framework that generates adversarial variants of MWPs by adding irrelevant variables. Fine-tuning on adversarial training instances improves performance on adversarial MWPs by 8%.
  arXiv  Detail & Related papers  (2024-05-30T18:07:13Z)
• MWPRanker: An Expression Similarity Based Math Word Problem Retriever [12.638925774492403]
  Math Word Problems (MWPs) in online assessments help test the ability of the learner to make critical inferences. We propose a tool in this work for MWP retrieval.
  arXiv  Detail & Related papers  (2023-07-03T15:44:18Z)
• Learning by Analogy: Diverse Questions Generation in Math Word Problem [21.211970350827183]
  Solving math word problem (MWP) with AI techniques has recently made great progress with the success of deep neural networks (DNN) We argue that the ability of learning by analogy is essential for an MWP solver to better understand same problems which may typically be formulated in diverse ways. In this paper, we make a first attempt to solve MWPs by generating diverse yet consistent questions/equations.
  arXiv  Detail & Related papers  (2023-06-15T11:47:07Z)
• Generalizing Math Word Problem Solvers via Solution Diversification [56.2690023011738]
  We design a new training framework for an MWP solver by introducing a solution buffer and a solution discriminator. Our framework is flexibly applicable to a wide setting of fully, semi-weakly and weakly supervised training for all Seq2Seq MWP solvers.
  arXiv  Detail & Related papers  (2022-12-01T19:34:58Z)
• Unbiased Math Word Problems Benchmark for Mitigating Solving Bias [72.8677805114825]
  Current solvers exist solving bias which consists of data bias and learning bias due to biased dataset and improper training strategy. Our experiments verify MWP solvers are easy to be biased by the biased training datasets which do not cover diverse questions for each problem narrative of all MWPs. An MWP can be naturally solved by multiple equivalent equations while current datasets take only one of the equivalent equations as ground truth.
  arXiv  Detail & Related papers  (2022-05-17T06:07:04Z)
• Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints [37.493809561634386]
  We study the problem of generating arithmetic math word problems (MWPs) given a math equation. Existing approaches are prone to generating MWPs that are mathematically invalid or have unsatisfactory language quality.
  arXiv  Detail & Related papers  (2021-09-09T20:24:25Z)
• Generate & Rank: A Multi-task Framework for Math Word Problems [48.99880318686938]
  Math word problem (MWP) is a challenging and critical task in natural language processing. We propose Generate & Rank, a framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions.
  arXiv  Detail & Related papers  (2021-09-07T12:21:49Z)
• MWP-BERT: A Strong Baseline for Math Word Problems [47.51572465676904]
  Math word problem (MWP) solving is the task of transforming a sequence of natural language problem descriptions to executable math equations. Although recent sequence modeling MWP solvers have gained credits on the math-text contextual understanding, pre-trained language models (PLM) have not been explored for solving MWP. We introduce MWP-BERT to obtain pre-trained token representations that capture the alignment between text description and mathematical logic.
  arXiv  Detail & Related papers  (2021-07-28T15:28:41Z)
• Are NLP Models really able to Solve Simple Math Word Problems? [7.433931244705934]
  We show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. We introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over sampled from existing datasets. The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.
  arXiv  Detail & Related papers  (2021-03-12T10:23:47Z)
• Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems [129.90766822085132]
  A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs. We propose a simple but efficient method called Universal Expression Tree (UET) to make the first attempt to represent the equations of various MWPs uniformly. Then a semantically-aligned universal tree-structured solver (SAU-r) based on an encoder-decoder framework is proposed to resolve multiple types of MWPs in a unified model.
  arXiv  Detail & Related papers  (2020-10-14T06:27:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.