README.md~

# A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

This repository contains the PyTorch implementation of the paper: **A Stack-Propagation Framework with 
Token-Level Intent Detection for Spoken Language Understanding**. 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>
@article{qin2019stack,
  title={A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding},
  author={Qin, Libo and Che, Wanxiang and Li, Yangming and Wen, Haoyang and Liu, Ting},
  journal={arXiv preprint arXiv:1909.02188},
  year={2019}
}
</pre>

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

## Architecture

<div align=center><img src="https://github.com/LeePleased/StackPropagation-SLU/blob/master/image/0.png" 
                   width="400" height="400" /></div>

## Preparation

Our code is based on PyTorch 1.1 and runnable for both windows and ubuntu server. Required python packages:
    
> + numpy==1.16.2
> + tqdm==4.32.2
> + scipy==1.2.1
> + torch==1.1.0
> + ordered-set==3.1.1

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. For reproducing the results reported in our
paper, We suggest you the following hyper-parameter setting for ATIS dataset:

        python trian.py -wed 256 -ehd 256 -aod 128 

Similarly, for SNIPS dataset, you can also consider the following command: 

        python trian.py -wed 32 -ehd 256 -aod 128

Due to some stochastic factors, It's necessary to slightly tune the hyper-parameters using grid search. If you have any question, please issue the project or email [me](yangmingli@ir.hit.edu.cn), we will reply you soon.