This generator generates Chinese poem automatically when given the first sentence or the initial characters. Basically, it's a rule based sequence to character model. 2 or 3 LSTMs are stacked together and trained as a generative model. To make poem more human-written alike, tone pattern rule is also added.
This is a group project for CSCI 544, Applied Natural Language Processing.
Members:
- Shengyu Chen
- Shuai Zhou
- Yizhao He
- Zichen Yang
We provide a User Interface to demo our Poem Generator: http:https://poem.knotesapp.com/
For fast testing, type in the first sentence: 春至花海棠 , leave intial characters empty and press Generate button
- python 3
- numpy, scipy
- yaml
- HDF5 and h5py
- TensorFlow
- Keras
python ./lstm/train.py [-m SAVE_PATH] [-d DATA_PATH] [-v]python ./lstm/generate.py [-p PRIME] [-s SENTENCE] [-v VOCAB_PATH] [-w MODEL_WEIGHTS_PATH] [-m MODEL_STRUCT_PATH]春至花海棠，
衰家夜有人，
一去见家悲，
今气又去禅。
The first version is a sequence to word language model based on LSTM programmed in Python with Keras.
All poems used in training are 5-character simplified Chinese poems written in Tang dynasty.
- data size: 16,000+ poems
- memory units number: 256 to 700
- dropout: 0.2
- batch size: 64 to 256
- epoch: 100+
- sequence length: 6 or 8
- number of LSTM layers: 2 to 3
All poetry data comes from JackyGao's work, which is a great contribution for this model. Peotries are labed with tone patterns which are the rules Chinese poems need to follow.
MIT