KETOD: Knowledge-Enriched Task-Oriented Dialogue

This repo contains the dataset from NAACL 2022 paper "KETOD: Knowledge-Enriched Task-Oriented Dialogue" https://arxiv.org/abs/2205.05589

Dataset Generation

KETOD is built upon the google SGD dataset. Here we release our knowledge-enriched utterances annotations and the script to generate the final dataset.

1. Go to https://github.com/google-research-datasets/dstc8-schema-guided-dialogue to download the SGD dataset.
2. Unzip ketod_release.zip and put with the SGD dataset in the same directory.
3. Edit the main entry of gen_ketod_data.py to set up your own data paths.
4. Run 'python gen_ketod_data.py' to generate the full KETOD dataset.

Dataset Format

Each entry of the data is one dialogue. It has the following fields:

"dialogue_id": unique id of the dialogue.

"turns": the list of dialogue turns. Besides the original fields in the SGD dataset, if it is an enriched turn, then we have the following additional fields:
    {
      "enrich": True. For turns without chitchat enrichment, this field is False. 
      "entity_query": The entity query we use to do knowledge retrieval.
      "enriched_utter": The utterance enriched with chitchat. Another field 'utterance' is the original response in the SGD dataset.
      "kg_snippets": the index of the ground truth knowledge snippets
      "kg_snippets_text": the content of the ground truth knowledge snippets
    }
  
"dialog_query": all the entity queries we use to do knowledge retrieval in this dialog

"entity_passages": all the wikipedia passages retrieved in this dialog

"entity_passage_sents": all the wikipedia passages retrieved in this dialog, breaked into snippets associated with index numbers

Code

To run the model, go to the "code" folder.

To run the knowledge selection model, go to "kg_selection" folder: run process_data.py first, then train the model with Train.py, generate the kg selection results with Test.py, for all train, dev, and test sets.

To run the SimpleToDPlus model, go to "simpletodplus" folder: modify and run gen_kg_train.py to generate data files with the kg selection results. Then run gen_data.py to generate train/dev/test files for the model input formats. Using the run_simpletod.sh script, run train_simpletod.py for training, and test_simpletod_simple.py for testing. You need to modify and follow the steps at the end of the test_simpletod_simple.py file to generate the results for each step.

Citation

If you find this project useful, please cite it using the following format

@inproceedings{DBLP:conf/naacl/ChenLMSCW22,
  author    = {Zhiyu Chen and
               Bing Liu and
               Seungwhan Moon and
               Chinnadhurai Sankar and
               Paul A. Crook and
               William Yang Wang},
  editor    = {Marine Carpuat and
               Marie{-}Catherine de Marneffe and
               Iv{\'{a}}n Vladimir Meza Ru{\'{\i}}z},
  title     = {{KETOD:} Knowledge-Enriched Task-Oriented Dialogue},
  booktitle = {Findings of the Association for Computational Linguistics: {NAACL}
               2022, Seattle, WA, United States, July 10-15, 2022},
  pages     = {2581--2593},
  publisher = {Association for Computational Linguistics},
  year      = {2022},
  url       = {https://doi.org/10.18653/v1/2022.findings-naacl.197},
  doi       = {10.18653/v1/2022.findings-naacl.197},
  timestamp = {Mon, 01 Aug 2022 16:27:57 +0200},
  biburl    = {https://dblp.org/rec/conf/naacl/ChenLMSCW22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

License

KETOD is released under MIT license, see LICENSE for details.