Skeleton-to-Response

Requirement

pytorch==0.3.1

Usage

• Cascaded Model
  1. pretrain skeleton generator: go to the template folder, use train.sh
  2. pretrain response generator: go to the pretrain folder, use train.sh.
  3. pretrain critic: go to the hard folder, use train_critic.sh
  4. train both skeleton generator and response generator with RL: go to the hard folder, use train.sh
  5. Test: go for hard/translate.sh
• Joint Model
  • Use the train.sh and translate.sh in the soft folder

Data

The data we used in our paper are from Wu et al, 2019

some sample data are in the data folder. The format is

query | response | retrieved query | retrieved response

(sentences in each line are split by the symbol |)

Citation

@inproceedings{cai-etal-2019-skeleton,
    title = "Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory",
    author = "Cai, Deng  and Wang, Yan  and Bi, Wei  and Tu, Zhaopeng  and Liu, Xiaojiang  and Lam, Wai  and Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1124",
    pages = "1219--1228"
}