Generative Knowledge Selection for Knowledge-Grounded Dialogues

Code for Generative Knowledge Selection for Knowledge-Grounded Dialogues, Weiwei Sun, Pengjie Ren, and Zhaochun Ren.

Knowledge selection for knowledge-grounded dialogue systems using generative language models. Benefit from better interaction between knowledge and between knowledge and multi-turn dialogues.

Data pre-processing

Code for pre-process wizard-of-wikipedia, holl-e, and cmu-dog can be found at process_wizard.py, process_holle.py, and process_cmu.py.

Training passage re-ranking model

Train a lightweight passage re-ranking model using contrastive objectives, and get predictions on the test set.

python psg_ranker.py

Training knowledge selection and response generation model

Train GenKS model that sequentially selects knowledge snippets and generates dialogue response using generative model (BART).

python run_genks.py

Cite

@inproceedings{Sun2023GenerativeKS,
  title={Generative Knowledge Selection for Knowledge-Grounded Dialogues},
  author={Weiwei Sun and Pengjie Ren and Zhaochun Ren}
  booktitle={Findings of EACL 2023},
  year={2023}
}