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https://github.com/squareRoot3/Target-Guided-Conversation
"Target-Guided Open-Domain Conversation" in ACL 2019
https://github.com/squareRoot3/Target-Guided-Conversation
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"Target-Guided Open-Domain Conversation" in ACL 2019
- Host: GitHub
- URL: https://github.com/squareRoot3/Target-Guided-Conversation
- Owner: squareRoot3
- Created: 2019-03-30T05:48:51.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-06-06T14:53:55.000Z (over 5 years ago)
- Last Synced: 2024-08-03T09:07:27.337Z (5 months ago)
- Language: Python
- Homepage: https://aclanthology.org/P19-1565/
- Size: 73.2 KB
- Stars: 148
- Watchers: 2
- Forks: 23
- Open Issues: 2
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
- StarryDivineSky - squareRoot3/Target-Guided-Conversation
README
# Target-Guided Open-Domain Conversation
This is the code for the following paper:
[Target-Guided Open-Domain Conversation](http://arxiv.org/abs/1905.11553)
*Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, Zhiting Hu; ACL 2019*
### Requirement
- `nltk==3.4`
- `tensoflow==1.12`
- `texar>=0.2.1` ([Texar](https://github.com/asyml/texar))
### Usage
#### Data Preparation
The dataset developed in the paper is on [google drive](https://drive.google.com/file/d/1oTjOQjm7iiUitOPLCmlkXOCbEPoSWDPX/view?usp=sharing). Download
and unzip it into `preprocess/convai2`. Then run the following command:
```shell
cd preprocess
python prepare_data.py
```
By default, the processed data will be put in the `tx_data` directory.
#### Turn-level Supervised Learning
In this project there are 5 different types of agents, including the kernel/neural/matrix/retrieval/retrieval_stgy agent,
which are all discribed in the paper. You can modify the configration of each agent in the `config` directory.
To train the kernel/neural/matrix agent, you need to first train/test the keyword prediction module,
and then train/test the retrieval module of each agent specified by the `--agent` parameter.
```shell
python train.py --mode train_kw --agent kernel
python train.py --mode train --agent kernel
python train.py --mode test --agent kernel
```
The retrieval agent and the retrieval_stgy agent share the same retrival module. You only need to train one of them:
```shell
python train.py --mode train --agent retrieval
python train.py --mode test --agent retrieval
```
#### Target-guided Conversation
After turn-level training, you can start target-guided conversation (human evaluation) with
the kernel/neural/matrix/retrieval/retrieval_stgy agent specified by the `--agent` parameter.
```shell
python chat.py --agent kernel
```
A demo of target-guided conversation (user is required to input the response after `HUMAN:`)
```
--------Session 0 --------
START: hello, how are you doing
HUMAN: hi i am great just finishing up some homework. how about you
AGENT: just got done eating some chicken and rice after i got done with my gym session
HUMAN: i am gonna eat some vegetable for dinner
AGENT: so you are vegetarian ? i am from los angeles so thereare lots of vegetarian restaurants
HUMAN: i am not a vegetarian . fried chicken is my favorite.
AGENT: oh boy , when i go to mcdonald i always get fries for my 2 dogs
Successfully chat to the target 'mcdonald'.
```
You can also watch the simulation of the target-guided conversation
between the retrieval agent pretending the user and the kernel/neural/matrix/retrieval_stgy agent specified by the `--agent` parameter. Simulation times are defined by the `--times` parameter. The program will calculate the success times and average turns in the end.
```shell
python simulate.py --agent kernel --times 100
```
A demo of simulation:
```
--------Session 0 --------
START: hey hey good morning to you
retrieval_agent: fine thanks . what do you do for a living ?
kernel_agent: rewarding job , i work at a hospital
Keyword: job, Similarity: 0.58
Successfully chat to the target 'hospital'.
...
--------Session 99 --------
START: hey hows it going ? i'm just cooking a steak
retrieval_agent: i'm thinking of a bbq sandwich for lunch
kernel_agent: nice i love to cook but now its just me and the fur babies
Keyword: baby, Similarity: 0.45
retrieval_agent: i love bagels however i own a dry cleaners
kernel_agent: i love animals felix my cat and my dog emmy
Keyword: cat, Similarity: 0.56
retrieval_agent: sounds awesome i have all kind of pets my family own a farm
kernel_agent: i love blue as well even my hair is blue
Keyword: blue, Similarity: 1.00
Successfully chat to the target 'blue'.
success time 83, average turns 4.28
```