Note: I don't provide personal support for custom changes in the code. Only for the release. For people just starting, I recommend Treehouse for online-learning.
Investigation of Recurrent Neural Network Architectures and Learning Methods for Spoken Language Understanding
Code for RNN and Spoken Language Understanding
Based on the Interspeech '13 paper:
We also have a follow-up IEEE paper:
Code
This code allows to get state-of-the-art results and a significant improvement (+1% in F1-score) with respect to the results presented in the paper.
In order to reproduce the results, make sure Theano is installed and the
repository is in your PYTHONPATH, e.g run the command
export PYTHONPATH=/path/where/is13/is:$PYTHONPATH. Then, run the following
commands:
git clone git@github.com:mesnilgr/is13.git
python is13/examples/elman-forward.py
For running the Jordan architecture:
python is13/examples/jordan-forward.py
ATIS Data
Download ATIS Dataset here! split 0 split 1 split 2 split 3 split 4
import cPickle
train, test, dicts = cPickle.load(open("atis.pkl"))
dicts is a python dictionnary that contains the mapping from the labels, the
name entities (if existing) and the words to indexes used in train and test
lists. Refer to this tutorial for more details.
Running the following command can give you an idea of how the data has been preprocessed:
python data/load.py
License
Recurrent Neural Network Architectures for Spoken Language Understanding by Grégoire Mesnil is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Based on a work at https://github.com/mesnilgr/is13.
