If you use this code as part of any published research, please acknowledge one of the following papers.
@inproceedings{chen2019sequential,
title={Sequential Matching Model for End-to-end Multi-turn Response Selection},
author={Chen, Qian and Wang, Wen},
booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7350--7354},
year={2019},
organization={IEEE}
}
@article{DBLP:journals/corr/abs-1901-02609,
author = {Chen, Qian and Wang, Wen},
title = {Sequential Attention-based Network for Noetic End-to-End Response Selection},
journal = {CoRR},
volume = {abs/1901.02609},
year = {2019},
url = {http://arxiv.org/abs/1901.02609},
}
Requirement
gensim
pip install gensim
Tensorflow 1.9-1.12 + Python2.7
Steps
Download the Ubuntu dataset released by (Xu et al, 2017)
Unzip the dataset and put data directory into data/
Preprocess dataset, including concatenatate context and build vocabulary
cd data
python prepare.py
Train word2vec
bash run_train_word2vec.sh
Train and test ESIM, the log information is in log.txt file. You could find an example log file in log_example.txt.
ESIM for Multi-turn Response Selection Task
Introduction
If you use this code as part of any published research, please acknowledge one of the following papers.
Requirement
gensim
Tensorflow 1.9-1.12 + Python2.7
Steps
Download the Ubuntu dataset released by (Xu et al, 2017)
Unzip the dataset and put data directory into
data/Preprocess dataset, including concatenatate context and build vocabulary
Train word2vec
Train and test ESIM, the log information is in
log.txtfile. You could find an example log file inlog_example.txt.