Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in dialogue generation（EMNLP2019）
Description
This repository hosts the Dir-VHRED model for dialogue generation as described by Min and Yisen et al.2019
requirements
- pandas==0.20.3
- numpy==1.14.0
- gensim==3.1.0
- spacy==1.9.0
- tqdm==4.15.0
- nltk==3.2.5
- tensorboardX==1.1
- torch==0.4
1.Prepare the dataset:
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
2.Train the model:
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
3.Evaluate the model(negative log-likelihood):
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
4.Evaluate the model(word-embedding metric):
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 #n_sample_step=1 means 1-turn while n_sample_step=3 means 3-turn responses
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
5.Generate the response:
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