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Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

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Slot-Gated Modeling for Joint Slot Filling and Intent Prediction


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

  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},

Want to Reproduce the experiment?


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

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

Your dataset should be seperated to three folders - train, test, and valid, which is named 'train', 'test', and 'valid' by default setting of Each of these folders contain three files - word sequence, slot label, and intent label, which is named '', 'seq.out', and 'label' by default setting of 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.


tensorflow 1.4
python 3.5


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

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

  • use "python3 -h" for all avaliable parameter settings

  • Note: must type

    . If you don't want to use this flag, type

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