Tensorflow implementation of joint modeling of SLU (intent & slot filling) and LM with RNNs.
This package implements joint model Number 14 and 15 in Table 1 of the below reference paper [1], i.e. the joint model with recurrent (& local) intent + slot label context. The other joint models in the paper can be made with simple modifications of generate_encoder_output.py and seq_labeling.py.
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
use_local_context=False # boolean, whether to use local context
DNN_at_output=True # boolean, set to True to use one hidden layer DNN at task output
python run_rnn_joint.py --data_dir $data_dir \
--train_dir $model_dir\
--max_sequence_length $max_sequence_length \
--use_local_context $use_local_context \
--DNN_at_output $DNN_at_outputReference
[1] Bing Liu, Ian Lane, "Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks", SIGdial, 2016 (PDF)
@InProceedings{liu-lane:2016:SIGDIAL,
author = {Liu, Bing and Lane, Ian},
title = {Joint Online Spoken Language Understanding and Language Modeling With Recurrent Neural Networks},
booktitle = {Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue},
month = {September},
year = {2016},
address = {Los Angeles},
publisher = {Association for Computational Linguistics},
pages = {22--30},
url = {http://www.aclweb.org/anthology/W16-3603}
}
Contact
Feel free to email [email protected] for any pertinent questions/bugs regarding the code.
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