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https://github.com/THUNLP-AIPoet/MixPoet
Source codes of MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space (AAAI 2020)
https://github.com/THUNLP-AIPoet/MixPoet
chinese poetry-generator style
Last synced: 3 months ago
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Source codes of MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space (AAAI 2020)
- Host: GitHub
- URL: https://github.com/THUNLP-AIPoet/MixPoet
- Owner: THUNLP-AIPoet
- Created: 2020-03-30T12:19:13.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-11-19T08:42:07.000Z (almost 4 years ago)
- Last Synced: 2024-07-11T04:34:03.636Z (4 months ago)
- Topics: chinese, poetry-generator, style
- Language: Python
- Size: 1.79 MB
- Stars: 145
- Watchers: 3
- Forks: 30
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
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README
# MixPoet
The source code of [*MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space*](https://arxiv.org/pdf/2003.06094.pdf) (AAAI 2020).
## 1. Rights
All rights reserved.
## 2. Requirements
* python>=3.7.0
* pytorch>=1.3.1
* sklearn>=0.19.2
* matplotlib>=2.2.3
## 3. Data Preparation
To train the model and generate poems, please
* add the training, validation and testing sets of our [THU-CCPC](https://github.com/THUNLP-AIPoet/Datasets/tree/master/CCPC) data into the *MixPoet/preprocess/* directory;
* add the pingsheng.txt, zesheng.txt, pingshui.txt and pingshui_amb.pkl files of our [THU-CRRD](https://github.com/THUNLP-AIPoet/Datasets/tree/master/CRRD) set into the *MixPoet/data/* directory.
We provide a small sampled subset of the labelled CQCF in the *preprocess* directory to test these codes. We will consider releasing the full CQCF set in the future. You can also use your own data.
## 4. Preprocessing
In Mixpoet/preprocess/, just run:
```
python preprocess.py --n 150000
```
The parameter *n* indicates the number of unlabelled instances to be used for semi-supervised training. When one uses the provided sampled CQCF set, we recommend *n* is about 150,000. With a larger labelled dataset, one can set a larger *n*.
After running preprocess.py, please move the generated vocab.pickle, ivocab.pickle, semi_train.pickle and semi_valid.pickle into *MixPoet/corpus/*, and move test_inps.txt and training_lines.txt into *MixPoet/data/*.
## 5. Training
In MixPoet/codes/, run:
```
python train.py
```
The encoder and decoder will be pre-trained as a denoising autoencoder, and the classifiers will be pre-trained with the labelled poems. Then the MixPoet model is trained based on these pre-trained modules.
One can also edit MixPoet/codes/**config.py** to modify the configuration, such as the hidden size, embedding size, data path, training epoch, learning rate and so on.
During the training process, some training information is outputed, such as:
The training and validation information is saved in MixPoet/log/.
## 6. Generation
To generate a poem in an interactive interface, in MixPoet/codes/, run:
```
python generate.py -v 1
```
Then one can input the keyword, length and factor labels, and then get the generated pome:
By running:
```
python generate.py -v 1 -s 1
```
one can manually select each generated line from the beam candidates.
To generate poems with an input testing file, which contains a set of keywords, run:
```
python generate.py -m file -l 5 -i ../data/test_inps.txt -o outs_5char.txt
```
where l = 5 or 7, indicating the 5-char-line or 7-char-line quatrains.
## 7. Cite
If you use our source code, please kindly cite this paper:
Xiaoyuan Yi, Ruoyu Li, Cheng Yang, Wenhao Li and Maosong Sun. 2020. MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space. In *Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence*, New York, USA.
The bib format is as follows:
```
@inproceedings{Mixpoet:20,
author = {Xiaoyuan Yi and Ruoyu Li and Cheng Yang and Wenhao Li and Maosong Sun},
title = {MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space},
year = "2020",
booktitle = {Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence},
address = {New York, USA}
}
```
## 8. System
This work is a part of the automatic Chinese poetry generation system, [THUAIPoet (Jiuge, 九歌)](https://jiuge.thunlp.cn) developed by Research Center for Natural Language Processing, Computational Humanities and Social Sciences, Tsinghua University (清华大学人工智能研究院, 自然语言处理与社会人文计算研究中心). Please refer to [THUNLP](https://github.com/thunlp) and [THUNLP Lab](http://nlp.csai.tsinghua.edu.cn/site2/) for more information.
## 9. Contact
If you have any questions, suggestions or bug reports, please feel free to email [email protected] or [email protected].