Code and sample data accompanying the paper Modeling Multi-turn Conversation with Deep Utterance Aggregation.
We release E-commerce Dialogue Corpus, comprising a training data set, a development set and a test set for retrieval based chatbot. The statistics of E-commerical Conversation Corpus are shown in the following table.
| Train | Val | Test | |
|---|---|---|---|
| Session-response pairs | 1m | 10k | 10k |
| Avg. positive response per session | 1 | 1 | 1 |
| Min turn per session | 3 | 3 | 3 |
| Max ture per session | 10 | 10 | 10 |
| Average turn per session | 5.51 | 5.48 | 5.64 |
| Average Word per utterance | 7.02 | 6.99 | 7.11 |
The full corpus can be downloaded from https://drive.google.com/file/d/154J-neBo20ABtSmJDvm7DK0eTuieAuvw/view?usp=sharing.
label \t conversation utterances (splited by \t) \t response
We also release our source code to help others reproduce our result
Our code is compatible with python2 so for all commands listed below python is python2
We strongly suggest you to use conda to control the virtual environment
-
Install requirement
pip install -r requirements.txt -
Pretrain word embedding
python train_word2vec.py ./ECD_sample/train embedding -
Preprocess the data
python PreProcess.py --train_dataset ./ECD_sample/train --valid_dataset ./ECD_sample/valid --test_dataset ./ECD_sample/test --pretrained_embedding embedding --save_dataset ./ECD_sample/all -
Train the model
bash train.sh
If you encounter some cuda issues, please check your environment. For reference,
Theano 0.9.0
Cuda 8.0
Cudnn 5.1
If you use this code please cite our paper:
@inproceedings{zhang2018dua,
title = {Modeling Multi-turn Conversation with Deep Utterance Aggregation},
author = {Zhang, Zhuosheng and Li, Jiangtong and Zhu, Pengfei and Zhao, Hai},
booktitle = {Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018)},
pages={3740--3752},
year = {2018}
}