GitHub - bloomberg/cnn-rnf: Convolutional Neural Networks with Recurrent Neural...
source link: https://github.com/bloomberg/cnn-rnf
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Convolutional Neural Networks with Recurrent Neural Filters
Author: Yi Yang
Contact: [email protected]
Basic description
This is the Python implementation of the recurrent neural filters for convolutional neural networks, described in
Yi Yang
"Convolutional Neural Networks with Recurrent Neural Filters"
EMNLP 2018
BibTeX
@inproceedings{yang2018convolutional,
title={Convolutional Neural Networks with Recurrent Neural Filters},
author={Yang, Yi},
booktitle={Proceedings of Empirical Methods in Natural Language Processing},
year={2018}
}
Dependencies
- TensorFlow
- Keras
- Optional: CUDA Toolkit for GPU programming.
We use the Stanford Sentiment Treebank (SST) datasets processed by Lei et al. (2015). Please put all the files of this directory into the data/sst_text_convnet folder.
Please download the pre-trained GloVe vectors and unzip it into the data folder.
Results
Running the code requires two steps:
-
Prepare the data and generate the required data files
# binary sentiment classification python proc_data.py data/stsa.binary.pkl # fine-grained sentiment classification python proc_data.py --train-path data/sst_text_convnet/stsa.fine.phrases.train \ --dev-path data/sst_text_convnet/stsa.fine.dev \ --test-path data/sst_text_convnet/stsa.fine.test \ data/stsa.fine.pkl
-
CNNs for sentiment classification with linear filters and recurrent neural filters (RNFs)
# binary sentiment classification python cnn_keras.py --filter-type linear data/stsa.binary.pkl python cnn_keras.py --filter-type rnf data/stsa.binary.pkl # fine-grained sentiment classification python cnn_keras.py --filter-type linear data/stsa.fine.pkl python cnn_keras.py --filter-type rnf data/stsa.fine.pkl
Hyperparameter tunning may be needed to achive the best results reported in the paper.
Unfortunately, I failed to find out how to entirely eliminate randomness for training Keras-based models. However, you should be easily able to achieve 89%+ and 52%+ accuracies with RNFs after a few runs.
Recurrent neural filters consistently outperform linear filters across different filter widths, by 3-4% accuracy.
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