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README.md
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---
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# MWP-T5: Fine-tuning with Numeracy for Math Word Problem Generation
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This repository contains the code for the
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paper [Fine-tuning with Numeracy for Math Word Problem Generation]() [[PDF]()] published at IEEE T4E 2023.
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<img src="img/MWP-T5.jpg">
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You can access our source code [here](https://github.com/yashichawla/mwp-t5). If you would like to cite our work, please use
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the following BiBTex entry:
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```
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```
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# Performance
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MWP-T5 achieves state-of-the-art performance on the MAWPS and PEN datasets. The following tables show the performance
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of MWP-T5 and other models on the MAWPS and PEN datasets. For more information, please refer to the paper.
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## MAWPS
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| Model Name | BLEU-4 | ROUGE-L | METEOR |
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|---------------------|:---------:|:---------:|:---------:|
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| seq2seq-rnn | 0.153 | 0.362 | 0.175 |
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| seq2seq-rnn + GLoVe | 0.592 | 0.705 | 0.412 |
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| seq2seq-tf | 0.554 | 0.663 | 0.387 |
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| GPT | 0.368 | 0.538 | 0.294 |
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| GPT-pre | 0.504 | 0.664 | 0.391 |
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| GPT2-mwp2eq | 0.596 | 0.715 | 0.427 |
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| **MWP-T5** | **0.885** | **0.930** | **0.930** |
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## PEN
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| Model Name | BLEU-4 | ROUGE-L | METEOR |
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|------------|:---------:|:---------:|:---------:|
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| **MWP-T5** | **0.669** | **0.768** | **0.772** |
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# Contact
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Please feel free to contact us by emailing us to report any issues or suggestions, or if you have any further
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questions.
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Contact: - [Yashi Chawla](mailto:yashi.chawla1@gmail.com)
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You can also contact the other maintainers listed below.
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- [Chakita Muttaraju](mailto:chakitapesu@gmail.com)
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- [Prajwal Anagani](mailto:prajwalanagani@gmail.com)
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- [Parimala S](mailto:parimalas2001@gmail.com)
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- [Gowri Srinivasa](mailto:gsrinivasa@pes.edu)
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