# GL-GIN

**Repository Path**: CharlieShark/GL-GIN

## Basic Information

- **Project Name**: GL-GIN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-08-13
- **Last Updated**: 2021-08-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling

This repository contains the official `PyTorch` implementation of the paper: [Libo Qin](http://ir.hit.edu.cn/~lbqin/), [Fuxuan Wei](https://awake020.github.io/), [Tianbao Xie](https://tianbaoxie.com), [Xiao Xu](https://looperxx.github.io/), [Wanxiang Che](http://ir.hit.edu.cn/~car/chinese.htm), [Ting Liu](http://ir.hit.edu.cn/~liuting/).

If you use any source codes or the datasets included in this toolkit in your work, please cite the following paper. The bibtex are listed below:
<pre>
@misc{qin2021glgin,
      title={GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling}, 
      author={Libo Qin and Fuxuan Wei and Tianbao Xie and Xiao Xu and Wanxiang Che and Ting Liu},
      year={2021},
      eprint={2106.01925},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
</pre>


In the following, we will guide you how to use this repository step by step.

## Architecture

![framework](pictures/main.png)

## Results

![results](pictures/results.png)

## Preparation

Our code is based on PyTorch 1.2 Required python packages:

-   numpy==1.19.1
-   tqdm==4.50.0
-   pytorch==1.2.0
-   python==3.6.12
-   cudatoolkit==9.2
-   fitlog==0.7.1
-   ordered-set==4.0.2

We highly suggest you using [Anaconda](https://www.anaconda.com/) to manage your python environment.

## How to run it
The script **train.py** acts as a main function to the project, you can run the experiments by the following commands.
```Shell
# MixATIS_clean dataset (ON GeForce RTX2080TI)
python train.py -g -bs=16 -dd=./data/MixATIS_clean  -sd=./save/MixATIS_clean -nh=4 -wed=128 -ied=128 -ehd=256 -sdhd=128 -dghd=64 -nldg=2 -sgw=2 -ne=200

# MixSNIPS_clean dataset (ON TITAN Xp)
python train.py -g -bs=16 -dd=./data/MixSNIPS_clean  -sd=./save/MixSNIPS_clean -nh=8 -wed=64 -ied=128 -ehd=256 -sdhd=128 -dghd=128 -nldg=2 -sgw=1 -ne=100
```

You can directly load the best models we saved:
```Shell
# MixATIS_clean dataset
python train.py -g -ne=0 -dd=./data/MixATIS_clean -sd=./save/MixATIS_best

# MixSNIPS_clean dataset
python train.py -g -ne=0 -dd=./data/MixSNIPS_clean -sd=./save/MixSNIPS_best
```



If you have any question, please issue the project or email [me](mailto:lbqin@ir.hit.edu.cn) or [fuxuanwei](mailto:fuxuanwei@ir.hit.edu.cn) and we will reply you soon.