https://github.com/MiuLab/SlotGated-SLU

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
https://github.com/MiuLab/SlotGated-SLU

intent-prediction joint-models natural-language-understanding slot-filling spoken-language-understanding

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Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

• Host: GitHub
• URL: https://github.com/MiuLab/SlotGated-SLU
• Owner: MiuLab
• Created: 2018-03-13T05:14:58.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-04-06T08:05:30.000Z (about 3 years ago)
• Last Synced: 2024-01-17T01:00:52.804Z (5 months ago)
• Topics: intent-prediction, joint-models, natural-language-understanding, slot-filling, spoken-language-understanding
• Language: Python
• Homepage:
• Size: 418 KB
• Stars: 300
• Watchers: 9
• Forks: 106
• Open Issues: 8
• Metadata Files:
  • Readme: README.md

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README

        
# Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

## Reference

Main paper to be cited ([Goo et al., 2018](https://www.csie.ntu.edu.tw/~yvchen/doc/NAACL18_SlotGated.pdf))

```

@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](https://arxiv.org/abs/1805.10190)) 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/slot_label_file 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 --no_early_stop --dataset=snips --model_type=intent_only

* 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.