TREA

Source code of “TREA: Tree-structure Reasoning Schema for Conversational Recommendation (ACL 2023)”

If you encounter problems, feel free to contact me (wendili@hust.edu.cn). I will reply to you as soon as possible.

The TG-redial part of codes are in the other branch "tgredial"

Run

To run the recommendation part. python run_publish.py -is_finetune mov

To run the generation part. python run_publish.py -is_finetune gen

Dataset

We publish the preprocessed dataset train_publish.json and test_publish.json

if you wanna use raw datasets from previous works train.json and test.json, you just need to set process_raw_data=True during the dataset initialization.

Citation

If you find this repo helpful, please cite the following:

@inproceedings{li-etal-2023-trea,
    title = "{TREA}: Tree-Structure Reasoning Schema for Conversational Recommendation",
    author = "Li, Wendi  and
      Wei, Wei  and
      Qu, Xiaoye  and
      Mao, Xian-Ling  and
      Yuan, Ye  and
      Xie, Wenfeng  and
      Chen, Dangyang",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.167",
    doi = "10.18653/v1/2023.acl-long.167",
    pages = "2970--2982",
}