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https://github.com/RUCAIBox/Context-Tuning
This is the repository for COLING 2022 paper "Context-Tuning: Learning Contextualized Prompts for Natural Language Generation".
https://github.com/RUCAIBox/Context-Tuning
natural-language-generation
Last synced: about 1 month ago
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This is the repository for COLING 2022 paper "Context-Tuning: Learning Contextualized Prompts for Natural Language Generation".
- Host: GitHub
- URL: https://github.com/RUCAIBox/Context-Tuning
- Owner: RUCAIBox
- License: mit
- Created: 2022-08-28T09:14:34.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-16T02:33:13.000Z (about 2 years ago)
- Last Synced: 2024-08-03T09:07:05.589Z (5 months ago)
- Topics: natural-language-generation
- Homepage:
- Size: 2.93 KB
- Stars: 11
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - RUCAIBox/Context-Tuning
README
# Context-Tuning
This is the repository for COLING 2022 paper "Context-Tuning: Learning Contextualized Prompts for Natural Language Generation". The implementation is completely based on our text generation library **[TextBox 2.0](https://github.com/RUCAIBox/TextBox)**.
## Installation
You should clone the TextBox repository and follow its [instructions](https://github.com/RUCAIBox/TextBox#installation).
```bash
git clone https://github.com/RUCAIBox/TextBox.git && cd TextBox
bash install.sh
```
## Dataset
The datasets ROCStories (roc), WritingPrompts (wp), WikiPlots (wikip), and ChangeMyView (cmv) can be downloaded at the link [https://huggingface.co/datasets/RUCAIBox/Story-Generation](https://huggingface.co/datasets/RUCAIBox/Story-Generation).
## Running Context-Tuning based on TextBox
For example, you can conduct Context-Tuning on roc dataset using this command:
```bash
python run_textbox.py --model=Context_Tuning --dataset=roc
```
You can use `--dataset=xxx` to specify the dataset name, such as `roc`, `wp`, `wikip`, and `cmv`.
Other hyperparameters can be changed in the [yaml](https://github.com/RUCAIBox/TextBox/blob/2.0.0/textbox/properties/model/context_tuning.yaml). The `prompt_generator` can be set to `bert` or `roberta`. The `semantic_mapping` can be set to `True` or `False`. The `prompt_length` of `efficient_kwargs` can also be changed at your will.
## Reference
```bibtex
@inproceedings{tang-etal-2022-context,
title = "Context-Tuning: Learning Contextualized Prompts for Natural Language Generation",
author = "Tang, Tianyi and
Li, Junyi and
Zhao, Wayne Xin and
Wen, Ji-Rong",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.552",
pages = "6340--6354",
}
```