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"
To run the recommendation part.
python run_publish.py -is_finetune mov
To run the generation part.
python run_publish.py -is_finetune gen
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.
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",
}