Kostis-S-Z / DL_4_SLAM

Using Deep Learning (RNNs) for Second Language Acquisition Modeling

Deep Learning for Second Language Acquisition Modeling

Using RNNs for Second Language Acquisition Modeling based on the 2018 competition by Duolingo http://sharedtask.duolingo.com/

Requirements

Python 3.5/7 and other libraries. To install everything needed please run

pip install requirements.txt

Execute

1. Unpack data

In the data folder give permissions to the unpacking script by running:

chmod 777 unpack_data.sh

then execute it to unpack the data in put it in a folder.

./unpack_data.sh

In the starter_code folder, unpack the glove.6B.50d.txt.tar.gz that contains pretrained word embeddings:

tar -xvzf glove.6B.50d.txt.tar.gz

2. Run count_features.py to go over all data and make a dictionary of the features and how many times each value appears (important for feature selection)

python3 count_features.py

3. Execute either lstm.py for a default run of a model or experiments.py to run different comparative experiments.

python3 lstm.py

or run baseline.py for Duolingo's baseline Logistic Regression model.