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Code for CR-GIS

The implementation of our paper accepted by COLING2022: CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling

venue status update

Usage

Requirements

Install the required libraries as follows:

  • python==3.6.12
  • torch==1.3.0+cu100
  • torch-geometric==1.3.2
  • nltk==3.4.5
  • fuzzywuzzy==0.18.2

Dataset

The preprocessed dataset can be available from Google Drive, and put into the data/ dir.

Training & Testing

  • Run bash train_on_opendialkg.sh for training CR-GIS on OpenDialKG dataset.

  • Run bash train_on_tgredial.sh for training CR-GIS on TGReDial dataset.

  • More details can be found in the two scripts.

Citation

If you find our work useful for your research, please kindly cite our paper as follows:

@inproceedings{zhou-etal-2022-cr,
    title = "{CR}-{GIS}: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling",
    author = "Zhou, Jinfeng  and
      Wang, Bo  and
      Yang, Zhitong  and
      Zhao, Dongming  and
      Huang, Kun  and
      He, Ruifang  and
      Hou, Yuexian",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.32",
    pages = "400--411"