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
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.
