CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation

This repository is the code and resources for the paper CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation

Instructions

This project is mainly implemented with following tools:

• Pytorch
• DGL
• The initial checkpoints of pretrained models come from Hugginface.

So if you want to run this code, you must have following preliminaries:

• Python 3 or Anaconda (mine is 3.8)
• Pytorch (mine is 1.11.0)
• transformers (a package for huggingface) (mine is 4.21.3)
• DGL (mine is 0.9)

It is worth mentioning that the installation of previous DGL is not so easy. I found out an interesting fact that if DGL is not compatible to Pytorch, when running this pytorch code with cuda it may give some interesting errors. I have no solution to this issue, as it depends on the individual environment.

Datasets and Resources

Directly Download Dataset and Resources

To reproduce our work you need to download following files:

• The raw data come from the paper CCM

You need to download the raw data only if you want to reproduce the dataset by yourself.

Preprocess Dataset From Scratch

Make sure you have resources/commonsense_conversation_dataset ready.

Download rel2words.txt from Dropbox, and put it to resources/rel2words.txt.

Run python preprocess.py --model_name_or_path=microsoft/unilm-base-cased to get the dataset at datasets/cadge.

The introduction of the dataset

The structure of datasetsshould be like this:

├── datasets/cadge
      └── `id2triple.txt`    
      └── `id2word.txt`     
      └── `rel2word.txt` 
      └── `testset.txt` 
      └── `trainset.txt` 
      └── `triple2id.txt`
      └── `valset.txt`
      └── `word2id.txt`

Quick Start

1. Install packages

pip install -r requirements.txt

And you have to install Pytorch from their homepage.

2. Collect Datasets and Resources

As mentioned above.

3. Run the code for training or testing

Please refer to the command examples listed in python_commands.sh:

For example, for our model:

bash run_train.sh

bash run_generate.sh

Revise the parameters according to your demand.

Notation

Nothing is difficult. For technical details please refer to our paper.

Citation

If you found this repository or paper is helpful to you, please cite our paper. Currently we only have arxiv citation listed as follows:

This is the arxiv citation:

@misc{zhanga2023cadge,
      title={CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation}, 
      author={Hongbo Zhanga and Chen Tang and Tyler Loakmana and Chenghua Lina and Stefan Goetze},
      year={2023},
      eprint={2305.06294},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}