This repository hosts the Dir-VHRED model for dialogue generation as described by Min and Yisen et al.2019
1.download the ubuntu corpus in http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/ & movie corpus in https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html
2.split the dataset with train/validation/test rate: 0.8,0.1,0.1
3.run code: python3 cornell_preprocess.py python3 ubuntu_preprocess.py
python3 train.py --data=ubuntu --batch_size=40 --eval_batch_size=40 --kl_annealing_iter=100000 --word_drop=0.25 --z_sent_size=3
python3 train.py --data=cornell --batch_size=40 --eval_batch_size=40 --kl_annealing_iter=20000 --word_drop=0.25 --z_sent_size=3
python3 eval.py --data=ubuntu --batch_size=40 --eval_batch_size=40 --z_sent_size=3 --checkpoint=xxxx
python3 eval.py --data=cornell --batch_size=40 --eval_batch_size=40 --z_sent_size=3 --checkpoint=xxxx
python3 eval_embed.py --data=ubuntu --batch_size=40 --eval_batch_size=40 --z_sent_size=3 --checkpoint=xxxx --beam_size=5 --n_sample_step=3
python3 eval_embed.py --data=cornell --batch_size=40 --eval_batch_size=40 --z_sent_size=3 --checkpoint=xxxx --beam_size=5 --n_sample-step=3
python3 generate_sentence.py --data=ubuntu --batch_size=40 --kl_annealing_iter=100000 --word_drop=0.25 --z_sent_size=3 --checkpoint=xxx
python3 generate_sentence.py --data=cornell --batch_size=40 --kl_annealing_iter=20000 --word_drop=0.25 --z_sent_size=3 --checkpoint=xxx