JointSLU: Joint Semantic Parsing for Spoken/Natural Language Understanding

A Keras implementation of the models described in [Hakkani-Tur et al. (2016)] (https://www.csie.ntu.edu.tw/~yvchen/doc/IS16_MultiJoint.pdf).

This model learns various RNN architectures (RNN, GRU, LSTM, etc.) for joint semantic parsing, where intent prediction and slot filling are performed in a single network model.

Content

• Requirements
• Getting Started
• Model Running
• Contact
• Reference

Requirements

1. Python
2. Numpy pip install numpy
3. Keras and associated Theano or TensorFlow pip install keras
4. H5py pip install h5py

Dataset

1. Train: word sequences with IOB slot tags and the intent label (data/atis.train.w-intent.iob)
2. Test: word sequences with IOB slot tags and the intent label (data/atis.test.w-intent.iob)

Getting Started

You can train and test JointSLU with the following commands:

  git clone --recursive https://github.com/yvchen/JointSLU.git
  cd JointSLU

You can run a sample tutorial with this command:

  bash script/run_sample.sh rnn theano 0 | sh

Then you can see the predicted result in sample/rnn+emb_H-50_O-adam_A-tanh_WR-embedding.test.3.

Model Running

To reproduce the work described in the paper. You can run the slot filling only experiment using BLSTM by:

  bash script/run_slot.sh blstm theano 0 | sh

You can run the joint frame parsing (intent prediction and slot filling) experiment using BLSTM by:

  bash script/run_joint.sh blstm theano 0 | sh

Contact

Yun-Nung (Vivian) Chen, y.v.chen@ieee.org

Reference

Main papers to be cited

@Inproceedings{hakkani-tur2016multi,
  author    = {Hakkani-Tur, Dilek and Tur, Gokhan and Celikyilmaz, Asli and Chen, Yun-Nung and Gao, Jianfeng and Deng, Li and Wang, Ye-Yi},
  title     = {Multi-Domain Joint Semantic Frame Parsing using Bi-directional RNN-LSTM},
  booktitle = {Proceedings of Interspeech},
  year      = {2016}
}