A TensorFlow implementation of Recurrent Neural Networks for Sequence Classification and Sequence Labeling
Tensorflow implementation of attention-based LSTM models for sequence classification and sequence labeling.
Updates - 2017/07/29
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
use_attention=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 \
--use_attention $use_attention
Reference
@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 [email protected] for any pertinent questions/bugs regarding the code.