Tensorflow implementation of attention-based LSTM models for sequence classification and sequence labeling.
Setup
- Tensorflow, version >= r0.9 (https://www.tensorflow.org/versions/r0.9/get_started/index.html)
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_rnnReference
- 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.