bzantium / Posterior-Knowledge-Selection

Learning to Select Knowledge for Response Generation in Dialog Systems

PostKS (Posterior Knowledge Selection)

Pytorch implementation of Learning to Select Knowledge for Response Generation in Dialog Systems

For decoder, I apply Hierarchical Gated Fusion Unit (HGFU) [Yao et al. 2017] and I only use three number of knowledges for the sake of code simplicity.

prerequisite

1. Install required packages

sh install.sh

2. Download and extract glove files and pickle as torch tensors.

sh download_glove.sh

Train model

If you run train, vocab.json and trained parameters will be saved. Then you can play demo.

python train.py --pre_epoch 5 --n_epoch 15 --n_batch 128

Play demo

python demo.py

You need to type three knowledges and utterance. Then bot will reply!

# example
Type first Knowledge: i'm very athletic.
Type second Knowledge: i wear contacts.
Type third Knowledge: i have brown hair.

you: hi ! i work as a gourmet cook .
bot(response): i don't like carrots . i throw them away . # reponse can change based on training.

• If you type "change knowledge" at (you), you can retype three knowledges.
• If you type "exit" at (you), you can terminate demo.

DataSet

• I only use "self_original_no_cands" in Persona-chat released by ParlAI