qiming-zou / textstyletransfer

Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus

This is implementation for the paper "Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus" accepted by NAACL 2019.

• Add folder Create folder data/, dump/, model/ and pretrained_model/ in the same level of src/

• Data prep Put data to data_folder

• data_folder: ../data/[data_type]/
• data_type: yelp/gyafc_family
• Put train/dev/test corpus in original/target style as corpus.(train/dev/test).(orig/tsf) Put pretrained embedding in text format to the path of embed_fn

• Running instructions

1. Data processing yelp data: python3 corpus_helper.py --data DATA --vec_dim 100 --embed_fn gyafc_family data: python3 corpus_helper.py --data DATA --tokenize --vec_dim 100 --embed_fn

• DATA: gyafc_family / yelp
• "--tokenize" only for gyafc_family data
• data path: ../data/DATA/corpus.(train/test).(orig/tsf)
• save path: pkl is saved to ../dump/DATA/, pkl files are (train/test)_(orig/tsf).pkl, tuned embedding is saved to tune_vec.txt

2. python3 style_transfer_rl.py -- data DATA

• DATA: gyafc_family / yelp

yelp data pretrain CUDA_VISIBLE_DEVICES=0,1 python3 style_transfer_RL.py --data_type yelp --max_sent_len 18 --lm_seq_length 18 --lm_epochs 5 --style_epochs 1 --pretrain_epochs 2 --beam_width 1 --pretrained_model_path best_pretrained_model --batch_size 32

yelp data RL CUDA_VISIBLE_DEVICES=0,1 python3 style_transfer_RL.py --data_type yelp --max_sent_len 18 --lm_seq_length 18 --use_pretrained_model --pretrained_model_path best_pretrained_model --rollout_num 2 --beam_width 1 --rl_learning_rate 1e-6 --batch_size 16 --epochs 1

gyafc_family pretrain CUDA_VISIBLE_DEVICES=0,1 python3 style_transfer_RL.py --data_type gyafc_family --max_sent_len 30 --lm_seq_length 30 --lm_epochs 8 --style_epochs 3 --pretrain_epochs 4 --beam_width 1 --pretrained_model_path best_pretrained_model --batch_size 32

gyafc_family RL CUDA_VISIBLE_DEVICES=2,3 python3 style_transfer_RL.py --data_type yelp --max_sent_len 30 --lm_seq_length 30 --use_pretrained_model --pretrained_model_path best_pretrained_model --rollout_num 2 --beam_width 1 --rl_learning_rate 1e-6 --batch_size 16 --epochs 1

3. Test yelp data CUDA_VISIBLE_DEVICES=2 python3 style_transfer_test.py --data_type yelp --max_sent_len 18 --lm_seq_length 18 --use_beamsearch_decode --beam_width 1 --model_path MODEL_PATH --output_path OUTPUT_PATH --batch_size 32

• MODEL_PATH: ../model/[DATA_TYPE]/model
• OUTPUT_PATH: the path where transferred sentences are saved

Hypeparameters:

In reinforcement learning, we use a combination rewards from style, semantic discriminator and language model as the training reward. You may want to change style_weight, semantic_weight and lm_weight in params.py to tune the model. The larger the weight is, the more dominant the corresponding metric is.

Also, both pretrain.py and style_transfer_RL.py enable model selection, the default method in the implementation is to select the model with the highest semantic reward with a preset style threshold. You may want to try other methods for model selection.

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If you're considering using our code, please cite our paper:

@article{gong2019reinforcement, title={Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus}, author={Gong, Hongyu and Bhat, Suma and Wu, Lingfei and Xiong, Jinjun and Hwu, Wen-mei}, journal={arXiv preprint arXiv:1903.10671}, year={2019} }

Gong H, Bhat S, Wu L, Xiong J, Hwu WM. Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus. arXiv preprint arXiv:1903.10671. 2019 Mar 26.