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https://github.com/nusnlp/nea
Neural Essay Assessor: An Automated Essay Scoring System Based on Deep Neural Networks
https://github.com/nusnlp/nea
Last synced: 17 days ago
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Neural Essay Assessor: An Automated Essay Scoring System Based on Deep Neural Networks
- Host: GitHub
- URL: https://github.com/nusnlp/nea
- Owner: nusnlp
- License: gpl-3.0
- Created: 2016-09-26T05:07:42.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-01-17T06:46:49.000Z (about 7 years ago)
- Last Synced: 2023-10-20T22:08:31.042Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 102 KB
- Stars: 195
- Watchers: 9
- Forks: 72
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Neural Essay Assessor #
An automatic essay scoring system based on convolutional and recurrent neural networks, including GRU and LSTM.
### Set Up ###
* Install Keras (with Theano backend)
* Prepare data
* Run train_nea.py
### Data ###
We have used 5-fold cross validation on ASAP dataset to evaluate our system. This dataset (training_set_rel3.tsv) can be downloaded from [here](https://www.kaggle.com/c/asap-aes/data). After downloading the file, put it in the [data](https://github.com/nusnlp/nea/tree/master/data) directory and create training, development and test data using ```preprocess_asap.py``` script:
```bash
cd data
python preprocess_asap.py -i training_set_rel3.tsv
```
### Options ###
You can see the list of available options by running:
```bash
python train_nea.py -h
```
### Example ###
The following command trains a model for prompt 1 in the ASAP dataset, using the training and development data from fold 0 and evaluates it.
```bash
THEANO_FLAGS="device=gpu0,floatX=float32" python train_nea.py
-tr data/fold_0/train.tsv
-tu data/fold_0/dev.tsv
-ts data/fold_0/test.tsv
-p 1 # Prompt ID
--emb embeddings.w2v.txt
-o output_dir
```
### Frequently Asked Questions ###
See our [FAQ](https://github.com/nusnlp/nea/blob/master/FAQ.md) page for a list of frequently asked questions. If the answer to your question is not there, contact me ([email protected]).
### License ###
Neural Essay Assessor is licensed under the GNU General Public License Version 3. Separate commercial licensing is also available. For more information contact:
* Kaveh Taghipour ([email protected])
* Hwee Tou Ng ([email protected])
### Publication ###
Kaveh Taghipour and Hwee Tou Ng. 2016. [A neural approach to automated essay scoring](http://aclweb.org/anthology/D/D16/D16-1193.pdf). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.