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Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation

Codebase for Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation.

This codebase is built based on NeuralDialog-LAED from Tiancheng Zhao.

Requirements

python 2.7
pytorch >= 0.3.0.post4
sklearn
nltk

Datasets

The data folder contains three datasets:

The data/word2vec includes GloVe word embeddings filtered by the words in training sets.

Training

Language Generation

You can run the following command to train a dispersed GM-VAE model on PTB:

LOG_DIR="logs/ptb/dgmvae"
python main_lm.py --model GMVAE --log_dir $LOG_DIR --beta 0.2

You can use --use_mutual True to add the mutual information term in objective.

Interpretable Text Generation

Unsupervised text generation by dispersed Gaussian Mixture VAE (DGM-VAE)

You can run the following command to train a dispersed GM-VAE model on DD and evaluate the interpretability by homogeneity:

LOG_DIR="logs/dd/gmvae"
python main_inter.py --data daily_dialog --data_dir data/daily_dialog --mult_k 3 --k 5 --latent_size 5 --model GMVAE --log_dir $LOG_DIR --beta 0.3 --use_mutual True --post_sample_num 1 --sel_metric obj --lr_decay False

Supervised text generation by dispersed Categorical Mixture VAE (DCM-VAE)

You can run the following command to train a supervised dispersed CM-VAE model on DD and evaluate the interpretability by accuracy:

LOG_DIR="logs/dd_sup/bmvae"
python main_supervised.py --data daily_dialog --data_dir data/daily_dialog --model BMVAE --log_dir $LOG_DIR --beta 0.6

Dialog Generation

You can run the following command to train a dispersed GM-VAE model on SMD for dialog generation:

LOG_DIR="logs/smd/dgmvae"
python main_stanford.py --data stanford --data_dir data/stanford --model AeED_GMM --log_dir $LOG_DIR --use_mutual True --beta 0.5 --freeze_step 7000

More examples of running baseline models could be found in scripts/test.sh.

Evaluation

Test a existing model

To run an existing model, you can:

  • Set the --forward_only argument to be True
  • Set the --load_sess argument to the path of the model folder in LOG_DIR
  • Run the script

Metrics such as BLEU and negative log-likelihood are calculated by running this script.

Test reverse perplexity

To test the reverse perplexity, you need to train a third-party language model in the synthetic training set and test in the real test set.

For example, you could use the awd-lstm-lm as the third-party language model:

Firstly, run the following scripts to split the generated sentences into training and validation sets, and copy the real test set.

MODEL_DIR="logs/ptb/dgmvae/xxx-main_lm.py"
python scripts/split_sampling_corpus.py --model_dir $MODEL_DIR

The training, validation and test sets are saved in the reverse_PPL directory under MODEL_DIR.

Secondly, train language model (for example, the awd-lstm-lm) in the synthetic dataset:

output_data_dir=$MODEL_DIR"/reverse_PPL"
python awd-lstm-lm/main.py --batch_size 20 --data $output_data_dir --dropouti 0.4 --dropouth 0.25 --seed 141 --epoch 20 --save PTB.pt > $ouput_result_path

You can use other language models, just replacing the training and validation sets by the synthetic data.

Test word-level KL divergence

You can run the following script to evaluate the word-level KL divergence between the synthetic set and the real training set:

MODEL_DIR="logs/ptb/dgmvae/xxx-main_lm.py"
python scripts/test_wKL.py --model_dir $MODEL_DIR --data_dir data/ptb