Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots

This repository contains the source code and datasets for the EMNLP 2020 paper Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots by Gu et al.

Our proposed Filtering before Iteratively REferring (FIRE) model has achieved a new state-of-the-art performance of knowledge-grounded response selection on the PERSONA-CHAT and CMU_DoG datasets.

Model overview

Results

Dependencies

Python 2.7
Tensorflow 1.4.0

Datasets

Your can download the datasets and their corresponding embedding and vocabulary files used in our paper from the following links.

• PERSONA-CHAT and its embedding and vocabulary files.
  
• CMU_DoG and its embedding and vocabulary files.
  

Unzip the datasets to the folder of data and run the following commands. The processed files are stored in data/personachat_processed/ or data/cmudog_processed/.

cd data
python data_preprocess_pc.py
python data_preprocess_cd.py

Then, unzip their corresponding embedding and vocabulary files to the folder of data/personachat_processed/ or data/cmudog_processed/.

Train a new model

Take PERSONA-CHAT as an example.

cd scripts
bash train_personachat.sh

The training process is recorded in log_FIRE_train_personachat_original.txt file.

Test a trained model

bash test_personachat.sh

The testing process is recorded in log_FIRE_test_personachat_original.txt file. And your can get a test_out_FIRE_personachat_original.txt file which records scores for each example. Run the following command and you can compute the metric of Recall.

python compute_recall.py

Cite

If you use the code and datasets, please cite the following paper: "Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots" Jia-Chen Gu, Zhen-Hua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu. EMNLP (2020)

@inproceedings{gu-etal-2020-filtering,
 title = "Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots",
 author = "Gu, Jia-Chen and
           Ling, Zhenhua and
           Liu, Quan and
           Chen, Zhigang and
           Zhu, Xiaodan",
 booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
 month = nov,
 year = "2020",
 address = "Online",
 publisher = "Association for Computational Linguistics",
 url = "https://www.aclweb.org/anthology/2020.findings-emnlp.127",
 pages = "1412--1422",
}

Acknowledgments

Thank ParlAI for providing the PERSONA-CHAT dataset.
Thank Xueliang Zhao for providing the processed CMU_DoG dataset used in their paper.

Contact

Please keep an eye on this repository if you are interested in our work.
Feel free to contact us (gujc@mail.ustc.edu.cn) or open issues.