Code and sample data accompanying the paper Modeling Multi-turn Conversation with Deep Utterance Aggregation.

Dataset

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.

Data template

label \t conversation utterances (splited by \t) \t response

Source Code

We also release our source code to help others reproduce our result

Instruction

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

Tips

If you encounter some cuda issues, please check your environment. For reference,

Theano 0.9.0
Cuda 8.0
Cudnn 5.1

Reference

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}
}