DISCLAIMER: This project is no longer maintained now. It is originally the final project of DATA130006 on NLP in Fudan University.

EssaySense (former name: AES) is an NLP project on Automated Essay Scoring, based on neural network technologies.

Authors: Zilong Liang and Jiancong Gao.

Several neural network models are included to modelling the scoring task, implemented using TensorFlow. Pre-trained models are also included based on ASAP-AES dataset. You can use this application to score English essays, or train new models by feeding your own datasets.

Python 3.5+, TensorFlow 1.4.1+ and NLTK 3.2+ are required to run neural network models, and click 6.7+ is required to run the command line application. You can install all of the dependencies through the following command, if you are using pip as your python package manager:

$ pip3 install -r requirements.txt

Besides, NLTK's punctuation metadata is needed to perform sentence tokenizing task. But do not bother to download it manually, because this application would detect or download it automatically.

For datasets part, this project uses training_set_rel3.tsv in ASAP-AES as essay dataset and glove.6B.50d.txt of GloVe project as word embedding dataset. They are about approximate 200MB totally, so you need prepare them yourself, and put them into: essaysense/datasets/.

Check out the root directory of this project. A Command Line Interface named essaysense-cli is delivered to run the project. It's developed based on click. This CLI application could perform several tasks, including listing avaliable models, training, evaluating and visualizing. The follwing block gives an overview on usage.

$ ./essaysense-cli --help  # Show help information.
$ ./essaysense-cli --version  # Show the current version of the app.

$ ./essaysense-cli show  # Show names of avaliable AES models.
$ ./essaysense-cli train (model-name) [--prompt (domain-id)]  # Train a model from the beginning.
$ ./essaysense-cli evaluate (model-name) [--prompt (domain-id)]  # Run test on a specific pre-trained model.
$ ./essaysense-cli visualize (model-name) [--prompt (domain-id)]  # Visualize training process in TensorBoard.

In detail, suppose that your want to see avaliable AES models, you can use the command show. Note that these model names are important if you want to train them:

$ ./essaysense-cli show
[Loading] Avaliable models...
1: cnn-cnn
2: cnn-lstm
3: lstm

Then, training models seems fairly easy:

$ ./essaysense-cli train lstm -p 7
[Loading] GloVe word vectors...
[Loading] ASAP-AES domain 7 dataset...
[Loading] ASAP-AES domain 7 dataset...
[Training] prompt-7-document-level-lstm-with-mot-pooling
Train:   1,   Loss: 0.058921,   QWK-on-dev-set: 0.228258
Train:   2,   Loss: 0.019410,   QWK-on-dev-set: 0.332542
Train:   3,   Loss: 0.018675,   QWK-on-dev-set: 0.173433
...

Note that -p option is short for --prompt, identifying which prompt of the ASAP-AES dataset you would like to feed the model as a train set, the default value is 1.

Evaluation can be performed after the model on a specific prompt is appropriately trained:

$ ./essaysense-cli evaluate cnn-cnn -p 2
[Loading] GloVe word vectors...
[Loading] ASAP-AES domain 2 dataset...
[Loading] ASAP-AES domain 2 dataset...
[Evaluating prompt-2-sentence-level-cnn] QWK-on-test-set: 0.4919167

This application also provides you a simple interface to access TensorBoard along with a specific trained model:

$ ./essaysense-cli visualize cnn-cnn -p 2
[Loading] GloVe word vectors...
[Loading] ASAP-AES domain 2 dataset...
[Loading] ASAP-AES domain 2 dataset...
[Visualizing] Calling Tensorboard...
TensorBoard 0.4.0 at http://localhost:6006 (Press CTRL+C to quit)

Obviously, you have to add TensorBoard to you $PATH enviroment so that the application could make it.

Note that both evaluating and visualizing tasks demand a trained model on a single prompt. If you call a brand-new model for evaluating or visualizing, It would reporting error:

$ ./essaysense-cli visualize lstm -p 5
[Loading] GloVe word vectors...
[Loading] ASAP-AES domain 5 dataset...
[Loading] ASAP-AES domain 5 dataset...
[Visualizing] Calling Tensorboard...
[Error] The model have not been trained. Please train first.

1. All of the dataset used for this project should be download manually following instructions above.
2. GloVe word representation resources are licensed under the Apache License, Version 2.0 and pre-trained embeddings are licensed under the Public Domain Dedication and License.
3. This project is developed under the MIT license.