A classical Chinese quatrain generator based on the RNN encoder-decoder framework.

Two 4-layer LSTM networks are used as encoder and decoder respectively. The encoder takes as input four keywords provided by a poem planner, and the decoder generates a quatrain character by character.

The original repository is here, where there are also a bunch of raw data files necessary to train the model. The raw data files were downloaded from the Internet, mostly from similar open source projects.

raw/
├── ming.all
├── pinyin.txt
├── qing.all
├── qsc_tab.txt
├── qss_tab.txt
├── qtais_tab.txt
├── qts_tab.txt
├── shixuehanying.txt
├── stopwords.txt
└── yuan.all

Python 2.7

TensorFlow 1.0

Jieba 0.38

Gensim 2.0.0

To begin with, you should process the raw data to generate the training data:

python data_utils.py

The TextRank algorithm may take many hours to run. Instead, you could choose to stop it early by typing ctrl+c to interrupt the iterations, when the progress shown in the terminal has remained stationary for a long time.

Then, generate the word embedding data using gensim Word2Vec model:

python word2vec.py

Now, type the following command and wait for several hours:

python train.py

Start the user interaction program in a terminal once the training has finished:

python main.py

Type in an input sentence each time and the poem generator will create a poem for you.