Capsule-NLU

Joint Slot Filling and Intent Detection via Capsule Neural Networks

This project provides tools for joint slot filling and intent detection via Capsule Neural Networks.

Details about Capsule-NLU can be accessed here, and the implementation is based on the Tensorflow library.

Quick Links

• Installation
• Usage
• Data
• Results
• Acknowledgements

Installation

For training, a GPU is recommended to accelerate the training speed.

Tensorflow

The code is based on Tensorflow 1.5. You can find installation instructions here.

Dependencies

The code is written in Python 3.5. Its dependencies are summarized in the file requirements.txt.

tensorflow_gpu==1.5.0

numpy==1.14.0

six==1.11.0

scikit_learn==0.21.2

You can install these dependencies like this:

pip3 install -r requirements.txt

Usage

• Run the full model on SNIPS-NLU dataset with default hyperparameter settings
  python3 train.py --dataset=snips
  

  Try run without early-stop python3 train.py --dataset=snips --no_early_stop --max_epochs=60

• Run the model without re-routing on SNIPS-NLU dataset
  python3 train.py --dataset=snips --model_type=without_rerouting

• For all available hyperparameter settings, use
  python3 train.py -h

Data

Format

Each dataset is a folder under the ./data folder, where each sub-folder indicates a train/valid/test split:

./data
└── snips
    ├── test
    │   ├── label
    │   ├── seq.in
    │   └── seq.out
    ├── train
    │   ├── label
    │   ├── seq.in
    │   └── seq.out
    └── valid
        ├── label
        ├── seq.in
        └── seq.out

In each sub-folder,

• label file contains the intent label.
  e.g. AddToPlaylist

• seq.in file contains utterances as the input sequences. Each line indicates one utterance and words are separated by a single space.
  e.g. add sabrina salerno to the grime instrumentals playlist

• seq.out file contains ground truth slot labels. Each line indicates a sequence of slot labels and the BIO tagging scheme is used.
  e.g. O B-artist I-artist O O B-playlist I-playlist O

Work on your own data

Prepare and organize your dataset in a folder according to the format and put it under ./data/ and use --dataset=foldername during training.

For example, your dataset is ./data/mydata, then you need to use the flag --dataset=mydata for train.py.
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.

Results

Model		SNIPS-NLU			ATIS
	Slot (F1)	Intent (Acc)	Overall (Acc)	Slot (F1)	Intent (Acc)	Overall (Acc)
CNN TriCRF	-	-	-	0.944	-	-
Joint Seq	0.873	0.969	0.732	0.942	0.926	0.807
Attention BiRNN	0.878	0.967	0.741	0.942	0.911	0.789
Slot-Gated Full Atten.	0.888	0.970	0.755	0.948	0.936	0.822
DR-AGG	-	0.966	-	-	0.914	-
IntentCapsNet	-	0.974	-	-	0.948	-
Capsule-NLU (our)	0.918	0.973	0.809	0.952	0.950	0.834

Acknowledgements

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

https://github.com/FudanNLP/Capsule4TextClassification

https://github.com/snipsco/nlu-benchmark/tree/master/2017-06-custom-intent-engines

Reference

@inproceedings{zhang2019joint,
  title={Joint slot filling and intent detection via capsule neural networks},
  author={Zhang, Chenwei and Li, Yaliang and Du, Nan and Fan, Wei and Yu, Philip S},
  booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)},
  year={2019}
}