A TensorFlow implementation of Recurrent Neural Networks for Sequence Classification and Sequence Labeling
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README.md

Attention-based RNN model for Spoken Language Understanding (Intent Detection & Slot Filling)

Tensorflow implementation of attention-based LSTM models for sequence classification and sequence labeling.

Setup

Usage:

data_dir=data/ATIS_samples
model_dir=model_tmp
max_sequence_length=50  # max length for train/valid/test sequence
task=joint  # available options: intent; tagging; joint
bidirectional_rnn=True  # available options: True; False

python run_multi-task_rnn.py --data_dir $data_dir \
      --train_dir   $model_dir\
      --max_sequence_length $max_sequence_length \
      --task $task \
      --bidirectional_rnn $bidirectional_rnn

Reference

  • Bing Liu, Ian Lane, "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling", Interspeech, 2016 (PDF)
@inproceedings{Liu+2016,
author={Bing Liu and Ian Lane},
title={Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-1352},
url={http://dx.doi.org/10.21437/Interspeech.2016-1352},
pages={685--689}
}

Contact

Feel free to email liubing@cmu.edu for any pertinent questions/bugs regarding the code.