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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.

Updates - 2017/07/29

  • Updated code to work with the latest TensorFlow API: r1.2
  • Code cleanup and formatting
  • Note that this published code does not include the modeling of output label dependencies. One may add a loop function as in the rnn_decoder function in TensorFlow example to feed emitted label embedding back to RNN state. Alternatively, sequence level optimization can be performed by adding a CRF layer on top of the RNN outputs.
  • The dataset used in the paper can be found at: We used the training set in the original ATIS train/test split, which has 4978 training samples. There are 15 test samples that have multiple intent labels for an utterance. We used the more frequent label (most likely, "flight") as the true label during evaluation.



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
use_attention=True # available options: True; False

python --data_dir $data_dir \
      --train_dir   $model_dir\
      --max_sequence_length $max_sequence_length \
      --task $task \
      --bidirectional_rnn $bidirectional_rnn \
      --use_attention $use_attention


  • Bing Liu, Ian Lane, "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling", Interspeech, 2016 (PDF)
author={Bing Liu and Ian Lane},
title={Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling},
booktitle={Interspeech 2016},


Feel free to email for any pertinent questions/bugs regarding the code.

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