canyuchen / imn

CIKM 2019: Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

Interactive Matching Network for Multi-Turn Response Selection

This repository contains the source code and datasets for the CIKM 2019 paper Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots by Gu et al.

Our proposed Interactive Matching Network (IMN) has achieved a new state-of-the-art performance on four large-scale datasets that are publicly available for research on multi-turn conversation.

Model overview

Results

Dependencies

Python 2.7
Tensorflow 1.4.0

Datasets

Your can download the processed datasets used in our paper here and unzip it to the folder of data.
Ubuntu_V1
Ubuntu_V2
Douban
Ecommerce

Train a new model

Take Ubuntu_V1 as an example.

cd scripts
bash ubuntu_train.sh

The training process is recorded in log_train_IMN_UbuntuV1.txt file.

Test a trained model

bash ubuntu_test.sh

The testing process is recorded in log_test_IMN_UbuntuV1.txt file. And your can get a ubuntu_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 and datasets, please cite the following paper: "Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots" Jia-Chen Gu, Zhen-Hua Ling, Quan Liu. CIKM (2019)

@inproceedings{Gu:2019:IMN:3357384.3358140,
 author = {Gu, Jia-Chen and 
           Ling, Zhen-Hua and 
           Liu, Quan},
 title = {Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots},
 booktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
 series = {CIKM '19},
 year = {2019},
 isbn = {978-1-4503-6976-3},
 location = {Beijing, China},
 pages = {2321--2324},
 url = {http://doi.acm.org/10.1145/3357384.3358140},
 doi = {10.1145/3357384.3358140},
 acmid = {3358140},
 publisher = {ACM},
}