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DeepUtteranceAggregation

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Modeling Multi-turn Conversation with Deep Utterance Aggregation (COLING 2018)

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main.py
model.py
PreProcess.py
README.md
requirements.txt
train.sh
train_word2vec.py

# Installation Guide

1. Get the code

git clone https://github.com/cooelf/DeepUtteranceAggregation

Downloads the entire project code from GitHub to your computer.

cd DeepUtteranceAggregation

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

<code>pip install -r requirements.txt</code>

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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Code and sample data accompanying the paper **[Modeling Multi-turn Conversation with Deep Utterance Aggregation.](https://arxiv.org/abs/1806.09102)**

## 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 <code>python2</code> so for all commands listed below python is <code>python2</code>

We strongly suggest you to use <code>conda</code> to control the virtual environment

* Install requirement

<code>pip install -r requirements.txt</code>

* Pretrain word embedding

<code>python train_word2vec.py ./ECD_sample/train embedding</code>

* Preprocess the data

<code>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</code>

* Train the model

<code>bash train.sh</code>

### 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}
}
```