Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots

This repository contains the source code and dataset for the EMNLP 2019 paper Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots by Gu et al.

Our proposed Dually Interactive Matching Network (DIM) has achieved a new state-of-the-art performance of response selection on the PERSONA-CHAT dataset.

Model overview

Results

Dependencies

Python 2.7
Tensorflow 1.4.0

Dataset

Your can download the PERSONA-CHAT dataset here or from ParlAI, and unzip it to the folder of data.
Run the following commands and the processed files are stored in data/personachat_processed/.

cd data
python data_preprocess.py

Then, download the embedding and vocab files here, and unzip them to the folder of data/personachat_processed/.

Train a new model

cd scripts
bash train.sh

The training process is recorded in log_DIM_train.txt file.

Test a trained model

bash test.sh

The testing process is recorded in log_DIM_test.txt file. And your can get a persona_test_out.txt file which records scores for each context-response pair. Run the following command and you can compute the metric of Recall.

python compute_recall.py

Cite

If you use the code, please cite the following paper: "Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots" Jia-Chen Gu, Zhen-Hua Ling, Xiaodan Zhu, Quan Liu. EMNLP (2019)

@inproceedings{gu-etal-2019-dually,
    title = "Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots",
    author = "Gu, Jia-Chen  and
              Ling, Zhen-Hua  and
              Zhu, Xiaodan  and
              Liu, Quan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1193",
    pages = "1845--1854",
}