This is the source code of Deep Attention Matching network (DAM), that is proposed for multi-turn response selection in the retrieval-based chatbot.
This is the source code of Deep Attention Matching network (DAM), that is proposed for multi-turn response selection in the retrieval-based chatbot.
DAM is a neural matching network that entirely based on attention mechanism. The motivation of DAM is to capture those semantic dependencies, among dialogue elements at different level of granularities, in multi-turn conversation as matching evidences, in order to better match response candidate with its multi-turn context. DAM will appear on ACL-2018, please find our paper at: http://acl2018.org/conference/accepted-papers/.
DAM is a neural matching network that entirely based on attention mechanism. The motivation of DAM is to capture those semantic dependencies, among dialogue elements at different level of granularities, in multi-turn conversation as matching evidences, in order to better match response candidate with its multi-turn context. DAM appears on ACL-2018, please find our paper at [http://aclweb.org/anthology/P18-1103](http://aclweb.org/anthology/P18-1103).
## __TensorFlow Version__
## __TensorFlow Version__
DAM is originally implemented with Tensorflow, which can be found at: https://github.com/baidu/Dialogue/DAM . We highly recommend using the PaddlePaddle Fluid version here as it supports parallely training with very large corpus.
DAM is originally implemented with Tensorflow, which can be found at: [https://github.com/baidu/Dialogue/DAM](https://github.com/baidu/Dialogue/DAM)(in progress). We highly recommend using the PaddlePaddle Fluid version here as it supports parallely training with very large corpus.
## __Network__
## __Network__
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@@ -32,27 +32,42 @@ We test DAM on two large-scale multi-turn response selection tasks, i.e., the Ub
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@@ -32,27 +32,42 @@ We test DAM on two large-scale multi-turn response selection tasks, i.e., the Ub
## __Usage__
## __Usage__
First, please download [data](https://pan.baidu.com/s/1hakfuuwdS8xl7NyxlWzRiQ"data") and unzip it:
Take the experiment on the Ubuntu Corpus v1 for Example.
1) Go to the `ubuntu` directory
```
cd ubuntu
```
2) Download the well-preprocessed data for training
```
```
cd data
sh download_data.sh
unzip data.zip
```
```
3) Execute the model training and evaluation by
If you want use well trained models directly, please download [models](https://pan.baidu.com/s/1pl4d63MBxihgrEWWfdAz0w"models") and unzip it:
```
```
cd output
sh train.sh
unzip output.zip
```
```
for more detailed explanation about the arguments, please run
Train and test the model by:
```
```
sh run.sh
python ../train_and_evaluate.py --help
```
```
4) Run test by
```
sh test.sh
```
and run the test for different saved models by using different argument `--model_path`.
Similary, one can carry out the experiment on the Douban Conversation Corpus by going to the directory `douban` and following the same procedure.
