---
title: "Empowering Bengali Education with AI: Solving Bengali Math"
description: "Automatically turning math word problems into equations has been studied mostly… 97.30% accuracy. 18 references, cited by 7 papers here."
authors: ["Jalisha Jashim Era", "Bidyarthi Paul", "Tahmid Sattar Aothoi", "Mirazur Rahman Zim", "Faisal Muhammad Shah"]
year: 2025
venue: "arXiv:2501.02599"
source: "https://arxiv.org/abs/2501.02599"
doi: "10.48550/arxiv.2501.02599"
arxiv: "2501.02599"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/era-2025-empowering-bengali-education-ai-solving/"
---
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# Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models

**Authors:** Jalisha Jashim Era, Bidyarthi Paul, Tahmid Sattar Aothoi, Mirazur Rahman Zim and Faisal Muhammad Shah · **Year:** 2025 · **Venue:** arXiv:2501.02599 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2501.02599) · [PDF](https://arxiv.org/pdf/2501.02599) · [DOI 10.48550/arxiv.2501.02599](https://doi.org/10.48550/arxiv.2501.02599) · [arXiv 2501.02599](https://arxiv.org/abs/2501.02599) · [OpenAlex](https://openalex.org/W4411173540)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [7](https://md.research.elamur.ai/free/en/papers/era-2025-empowering-bengali-education-ai-solving/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Automatically turning math word problems into equations has been studied mostly for English and Chinese. Bengali, a low-resource language, had no dedicated dataset for this task. The authors address this gap by building "PatiGonit", a set of 10,000 elementary-level Bengali word problems. Native speakers translated a subset of MAWPS problems into Bengali and localized them, adapting numerals, currency and terminology. Each item pairs a problem text with its target equation. The authors treat solving as sequence-to-sequence generation: a model outputs the equation, and a separate equation solver computes the numeric answer. They fine-tuned and compared four transformer models: a Basic Transformer trained from scratch, mT5, BanglaT5 and mBART50, while varying learning rate, epochs and batch size. The pretrained multilingual models clearly outperformed the from-scratch baseline. mT5 performed best, with mBART50 nearly matching it. The work provides the first Bengali benchmark for this task and indicates that pretrained seq2seq models transfer well to equation generation in low-resource educational settings.

## Key points
- **Dataset:** PatiGonit contains 10,000 problems split 80/10/10 into train, validation and test sets.
  - 9,752 problems need a single operation: 1,761 addition, 3,217 subtraction, 1,610 multiplication and 3,164 division.
  - Only 248 problems combine several operators.
- **Best result:** mT5 achieved the top scores, 97.30% accuracy alongside 94.68 BLEU, using batch size 8 and 15 epochs.
- **Model comparison:** mBART50 scored 97.20%, and BanglaT5 was close behind. The Basic Transformer peaked at 77.30%.
- **Source data:** The problems were taken from MAWPS, which the paper describes as containing 38,000 items. Only 10,000 were translated because of resource limits.
- **Limitations:**
  - The dataset is dominated by simple arithmetic, so multi-step problems are underrepresented.
  - Translation introduced ambiguities, for example with foreign names and cultural references.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

Also on the byline: [Ahsanullah University of Science and Technology](https://ror.org/04wfbp123).

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [🧮 Computational Theory and Mathematics](https://md.research.elamur.ai/free/en/shelves/computer-science/computational-theory-and-mathematics/) · 252 works

🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works

## Cite

Cite as: Jalisha Jashim Era, Bidyarthi Paul, Tahmid Sattar Aothoi, Mirazur Rahman Zim and Faisal Muhammad Shah. 2025. Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models. arXiv:2501.02599.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{era2025empowering,
  title = {{Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models}},
  author = {Jalisha Jashim Era, Bidyarthi Paul, Tahmid Sattar Aothoi, Mirazur Rahman Zim and Faisal Muhammad Shah},
  year = {2025},
  doi = {10.48550/arxiv.2501.02599},
  eprint = {2501.02599},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.02599}
}
```

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## References

18 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Patel, Arkil, Satwik Bhattamishra, and Navin Goyal. ”[Are NLP models really able to solve simple math word problems?](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/).” arXiv preprint arXiv:2103.07191 (2021).
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14. Niyarepola, Kashyapa, et al. ”Math word problem generation with multilingual language models.” Proceedings of the 15th International Conference on Natural Language Generation. 2022. [find](https://scholar.google.com/scholar?q=Niyarepola%2C+Kashyapa%2C+et+al%2E+%E2%80%9DMath+word+problem+generation+with+multilingual+language+models%2E%E2%80%9D+Proceedings+of+the+15th+International+Conference+on+Natural+Language+Generation%2E+2022%2E)
15. Xue, L. ”[mt5: A massively multilingual pre-trained text-to-text transformer](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/).” arXiv preprint arXiv:2010.11934 (2020).
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18. Luo, Liangchen, et al. ”[Improve Mathematical Reasoning in Language Models by Automated Process Supervision](https://md.research.elamur.ai/free/en/papers/luo-2024-improve-mathematical-reasoning-language-models/).” arXiv preprint arXiv:2406.06592 (2024).

*Updated 31 October 2026*