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title: README
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# 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)
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