---
title: README
emoji: 🦀
colorFrom: gray
colorTo: blue
sdk: static
pinned: false
---

# MWP-T5: Fine-tuning with Numeracy for Math Word Problem Generation

This repository contains the code for the
paper [Fine-tuning with Numeracy for Math Word Problem Generation]() [[PDF]()] published at IEEE T4E 2023.

<img src="MWP-T5.jpg">

You can access our source code [here](https://github.com/yashichawla/mwp-t5). If you would like to cite our work, please use
the following BiBTex entry:

```

```

# Performance

MWP-T5 achieves state-of-the-art performance on the MAWPS and PEN datasets. The following tables show the performance
of MWP-T5 and other models on the MAWPS and PEN datasets. For more information, please refer to the paper.

## MAWPS

| Model Name          |  BLEU-4   |  ROUGE-L  |  METEOR   |
|---------------------|:---------:|:---------:|:---------:|
| seq2seq-rnn         |   0.153   |   0.362   |   0.175   |
| seq2seq-rnn + GLoVe |   0.592   |   0.705   |   0.412   |
| seq2seq-tf          |   0.554   |   0.663   |   0.387   |
| GPT                 |   0.368   |   0.538   |   0.294   |
| GPT-pre             |   0.504   |   0.664   |   0.391   |
| GPT2-mwp2eq         |   0.596   |   0.715   |   0.427   |
| **MWP-T5**          | **0.885** | **0.930** | **0.930** |

## PEN

| Model Name |  BLEU-4   |  ROUGE-L  |  METEOR   |
|------------|:---------:|:---------:|:---------:|
| **MWP-T5** | **0.669** | **0.768** | **0.772** |

# Contact

Please feel free to contact us by emailing us to report any issues or suggestions, or if you have any further
questions.

Contact: - [Yashi Chawla](mailto:yashi.chawla1@gmail.com)

You can also contact the other maintainers listed below.

- [Chakita Muttaraju](mailto:chakitapesu@gmail.com)
- [Prajwal Anagani](mailto:prajwalanagani@gmail.com)
- [Parimala S](mailto:parimalas2001@gmail.com)
- [Gowri Srinivasa](mailto:gsrinivasa@pes.edu)