This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation", if you find this useful and use it for your own research, please cite us.

1. Download and unzip the repository.
2. Create a new conda environment and install the required libraries from the requirements.txt file.

conda create -n k2t python=3.6
conda activate k2t
pip install -r requirements.txt

A GPU will be required to run the experiments. Make sure you have a results folder.

Uncomment the appropriate lines of run.sh to run the hyperparameter experiments from the paper. For example,

python main.py -mode='next' -file_name=/data/50_keywordsets_eval/word_sets.txt -results_subfolder=guide_vs_no_guide_beams -weight=10.0 -top_p=0.9 -n_generated_sentences=90 -do_guarantee=True

runs K2T with ordered guide words (mode='next') on the random keywords dataset. It runs with lambda=weight=10, nucleus sampling with top-p=0.9, number of generated tokens = 90, and no weight annealing to guarantee word appearance. The results are saved in results/tmp

Uncomment the appropriate line of run.sh to run the model on the ROC story dataset:

python main.py -mode='max' -file_name=/data/ROC/ROCStories_20_storylines_500_0.txt -results_subfolder=final4_ -weight=5.0 -top_p=0.9 -n_generated_sentences=-7 -n_beams=4 -do_guarantee=True -task='ROC'

Uncomment the appropriate line of run.sh to run the model on the News Article story dataset:

python main_DBS.py -mode='max' -file_name=/data/keyword_to_articles -results_subfolder=tmp -weight=5.0 -top_p=0.9 -n_generated_sentences=-15 -n_beams=4 -do_guarantee=True -task='key2article'

├── data
│   ├── 50_keywordsets_eval
│   │   └── word_sets.txt
│   ├── keyword_to_articles
│   │   ├── test_10.txt
│   │   ├── test_12.txt
│   │   ├── test_13.txt
│   │   ├── test_14.txt
│   │   ├── test_15.txt
│   │   ├── test_16.txt
│   │   ├── test_4.txt
│   │   ├── test_5.txt
│   │   ├── test_8.txt
│   │   └── test_9.txt
│   └── ROC
│       └── ROCStories_20_storylines_500_0.txt
├── encode_keywords.py
├── encode_keywords_word2vec.py
├── main.py
├── metrics_degen.py
├── metrics_degen_run.sh
├── perplexity.py
├── README.md
├── requirements.txt
├── results
├── run.sh
└── utility_gpt.py