Datasets › ParaMAWPS

ParaMAWPS (Paraphrased Math Word Problem Solving Repository)

Introduced by Syed Rifat Raiyan et al. in Math Word Problem Solving by Generating Linguistic Variants of Problem Statements24 Jun 2023 archive 2025-07-28

This repository contains the code, data, and models of the paper titled "Math Word Problem Solving by Generating Linguistic Variants of Problem Statements" published in the Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop).

The work is outlined in a more detailed and expository manner in our Bachelor of Science (B.Sc.) thesis titled "Variational Mathematical Reasoning: Enhancing Math Word Problem Solvers with Linguistic Variants and Disentangled Attention" which can be accessed from the Islamic University of Technology (IUT) Institutional Repository.

[arXiv] [anthology] [GoogleScholar] [ResearchGate] [HuggingFace]

[PDF] [Slides] [Video] [Poster] [Thesis]

License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

[license]

Dataset

In order to download our dataset PᴀʀᴀMAWPS, please navigate to the ParaMAWPS folder. We use an 80:10:10 train-validation-test split for our PᴀʀᴀMAWPS dataset. The splits are available in .json format in the aforementioned folder.

Data Format

Each row consists of a Math Word Problem (MWP). The table below describes what each column signifies.

Column Title Description
id The unique identification number of the sample. Seed problems have id size of ≤4, whereas, variant problems have id size of > 4. The last variant of a seed problem (generally with the id "16000i", where i is the id of the seed problem) is the inverse variant of the seed problem.
original_text The problem statement of the MWP. The seed problems have the same problem statement as present in the Mᴀᴡᴘs dataset.
equation The equation with a variable x which solves the MWP
quantity_tagged_text The problem statement of the MWP, where each quantity is replaced with a unique tag [Qᵢ]
quantity_tagged_equation The equation with a variable x which solves the MWP, but each quantity is replaced with its unique tag [Qᵢ] in the problem statement
have_constant Whether the use of a constant value is required to solve the MWP
For an MWP sample i with have_constant label Cᵢ, the boolean label is,
$C_i =\begin{cases} \text{FALSE}, & \text{if i requires $0$ constant values}\\ \text{TRUE}, & \text{if i requires ≥1 constant values}\end{cases}$

Types of Variations

[drawing]

Dataset Statistics

[drawing] [drawing]

Methodology

[drawing]

Results

To reproduce the results, please refer to the documentation of MWPToolkit created by Yihuai Lan et al. [drawing] [drawing]

Citation

If you find this work useful, please cite our paper:

@inproceedings{raiyan-etal-2023-math,
    title = "Math Word Problem Solving by Generating Linguistic Variants of Problem Statements",
    author = "Raiyan, Syed Rifat  and
      Faiyaz, Md Nafis  and
      Kabir, Shah Md. Jawad  and
      Kabir, Mohsinul  and
      Mahmud, Hasan  and
      Hasan, Md Kamrul",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-srw.49",
    doi = "10.18653/v1/2023.acl-srw.49",
    pages = "362--378",
    abstract = "The art of mathematical reasoning stands as a fundamental pillar of intellectual progress and is a central catalyst in cultivating human ingenuity. Researchers have recently published a plethora of works centered around the task of solving Math Word Problems (MWP) {---} a crucial stride towards general AI. These existing models are susceptible to dependency on shallow heuristics and spurious correlations to derive the solution expressions. In order to ameliorate this issue, in this paper, we propose a framework for MWP solvers based on the generation of linguistic variants of the problem text. The approach involves solving each of the variant problems and electing the predicted expression with the majority of the votes. We use DeBERTa (Decoding-enhanced BERT with disentangled attention) as the encoder to leverage its rich textual representations and enhanced mask decoder to construct the solution expressions. Furthermore, we introduce a challenging dataset, ParaMAWPS, consisting of paraphrased, adversarial, and inverse variants of selectively sampled MWPs from the benchmark Mawps dataset. We extensively experiment on this dataset along with other benchmark datasets using some baseline MWP solver models. We show that training on linguistic variants of problem statements and voting on candidate predictions improve the mathematical reasoning and robustness of the model. We make our code and data publicly available.",
}

You can also cite our thesis:

@phdthesis{raiyan2023variational,
  type={Bachelor's Thesis},
  title={Variational Mathematical Reasoning: Enhancing Math Word Problem Solvers with Linguistic Variants and Disentangled Attention},
  author={Raiyan, Syed Rifat and Faiyaz, Md Nafis and Kabir, Shah Md Jawad},
  year={2023},
  school={Department of Computer Science and Engineering (CSE), Islamic University of Technology},
  address={Board Bazar, Gazipur-1704, Dhaka, Bangladesh},
  note={Available at \url{http://103.82.172.44:8080/xmlui/handle/123456789/2092}}
}

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Math Word Problem Solving ParaMAWPS DeBERTa (VM) Accuracy (%) 79.1 Math Word Problem Solving by Generating Linguistic... starscream-11813/variational-mathematical-reasoning 6 Compare

Papers archive 2025-07-28

2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 2. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Math Word Problem Solving by Generating Linguistic Variants of Problem Statements 1 5 24 Jun 2023 not harvested
DeBERTa: Decoding-enhanced BERT with Disentangled Attention 14 1 5 Jun 2020 ran 4 of 13 samples (9 unverified; 3 pointer-only for licence)

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • ParaMAWPS

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections