Need help with SlotGated-SLU?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

MiuLab
227 Stars 89 Forks 6 Commits 7 Opened issues

Description

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

Services available

!
?

Need anything else?

Contributors list

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

Reference

Main paper to be cited (Goo et al., 2018)

@inproceedings{goo2018slot,
  title={Slot-Gated Modeling for Joint Slot Filling and Intent Prediction},
    author={Chih-Wen Goo and Guang Gao and Yun-Kai Hsu and Chih-Li Huo and Tsung-Chieh Chen and Keng-Wei Hsu and Yun-Nung Chen},
    booktitle={Proceedings of The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
    year={2018}
}

Want to Reproduce the experiment?

Enter

--dataset=atis
or
--dataset=snips
to use ATIS or Snips (Coucke et al., 2018) dataset.

Where to Put My Dataset?

You need to put your dataset under ./data/ and use

--dataset=foldername
. For example, your dataset is ./data/mydata, then you need to enter
--dataset=mydata

Your dataset should be seperated to three folders - train, test, and valid, which is named 'train', 'test', and 'valid' by default setting of train.py. Each of these folders contain three files - word sequence, slot label, and intent label, which is named 'seq.in', 'seq.out', and 'label' by default setting of train.py. For example, the full path to train/slotlabelfile is './data/mydata/train/seq.out' .
Each line represents an example, and slot label should use the IBO format.
Vocabulary files will be generated by utils.createVocabulary() automatically
You may see ./data/atis for more detail.

Requirements

tensorflow 1.4
python 3.5

Usage

some sample usage
* run with 32 units, atis dataset and no patience for early stop
 python3 train.py --num_units=32 --dataset=atis --patience=0

  • disable early stop, use snips dataset and use intent attention version
     python3 train.py --noearlystop --dataset=snips --modeltype=intentonly

  • use "python3 train.py -h" for all avaliable parameter settings

  • Note: must type

    --dataset
    . If you don't want to use this flag, type
    --dataset=''
    instead.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.