KnowledGPT

This is an implementation of our paper

• Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao and Rui Yan. Knowledge-Grounded Dialogue Generation with Pre-trained Language Models. In EMNLP, 2020 (Main Conference)

Requirements

• Python 3.6
• Pytorch 1.2.0
• CUDA 10.0 supported GPU with at least 12GB memory
• see requirements.txt for more details

Usage

To run our pretrained model on Wizard of Wikipedia:

• Download the data from ParlAI, then preprocess the data

python preprocess.py --in_file /path/to/wizard_of_wikipedia/test_random_split.json --out_file wizard_of_wikipedia/data/test_seen.jsonl
python preprocess.py --in_file /path/to/wizard_of_wikipedia/test_topic_split.json --out_file wizard_of_wikipedia/data/test_unseen.jsonl

• Prepare the BERT/GPT-2 files

python bert_config.py --out_file pretrain-models/bert_base_uncased
python gpt2_config.py --out_file pretrain-models/gpt2

• Download the checkpoint from here, then save to "wizard_of_wikipedia/checkpoints"

• To evaluate the model, run

python evaluate.py --eval_batch_size 2 --gpu_list 0 --exp_name test

Citation

We appreciate your citation if you find our code is beneficial.

@inproceedings{zhao2020knowledgpt,
  title={Knowledge-Grounded Dialogue Generation with Pre-trained Language Models},
  author={Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, Rui Yan},
  booktitle = {EMNLP},
  year = {2020}
}