mwpt5/t5-mawps-pen
Text2Text Generation
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MWP-T5: Fine-tuning with Numeracy for Math Word Problem Generation
AI & ML interests
MWP generation, language modelling, representation learning
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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.
You can access our source code here. 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
You can also contact the other maintainers listed below.