This is the fourth and final assignment for NLP (CSE-538) in Fall 19 at Stony Brook University where we implement a bi-directional GRU as well as an original custom model for Relation Extraction:

The GRU is loosely based on the approach done in the work of Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (Zhou et. al, 2016).

We implement:

1. Bidirectional GRU
2. Attention layer
3. L2 Regularization

Additionally, we design our own network architecture to solve this task.

This assignment is implemented in python 3.6 and tensorflow 2.0. Follow these steps to setup your environment:

1. Download and install Conda
2. Create a Conda environment with Python 3.6

conda create -n nlp-hw4 python=3.6

3. Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use this code.

conda activate nlp-hw4

4. Install the requirements:

pip install -r requirements.txt

5. Download spacy model

python -m spacy download en_core_web_sm

6. Download glove wordvectors:

./download_glove.sh

We are given training and validation data in the form of text files. Training can be found in data/train.txt and validation is in data/val.txt. We are using data from a previous SemEval shared task which in total had 8,000 training examples. Your train/validation examples are a 90/10 split from this original 8,000. More details of the data can be found in the overview paper SemEval-2010 task 8: multi-way classification of semantic relations between pairs of nominals (Hendrickx et. al, 2009) as well as extra PDFs explaining the details of each relation in the dataset directory.

We have 4 main scripts in the repository train_basic.py, train_advanced.py, predict.py and evaluate.pl.

• Train scripts do as described and saves your model to be used later for prediction. Basic training script trains the basic MyBasicAttentiveBiGRU model which you are supposed implement (Bi-RNN+attention). You should not need to change this script. Advanced training script on other hand, is a template/starter code which you can adapt based on your MyAdvancedModel architecture design.

• Predict generates predictions on the test set test.txt and saves your output to a file. You will submit your predictions to be scored against the hidden (labels) test set. Both files are set with reasonable defaults in their arguments but example commands are shown below.

• Evaluation script is the pearl script unlike others. You can use it see detailed report of your predictions file against the gold labels.

python train.py --embed-file embeddings/glove.6B.100D.txt --embed-dim 100 --batch-size 10 --num_epochs 5

# stores the model by default at : serialization_dirs/basic/

python predict.py --prediction-file my_predictions.txt --batch-size 10

Experiments with the GRU:

1. Run with only Word Embeddings (remove pos_inputs and dependency structure. Removing dep structure can be done by setting shortest_path = [] in data.py)
2. Run with only Word + Pos embeddings
3. Run with only Word + Dep structure

Take a look at the report.pdf to find my report and findings as part of this assignment.