This is the GitHub repository is for the Course Project of DS-GA 1011 Natural Language Processing Final Project (2022) at New York University. Topics include: NLP, Sentiment Prediction, and Rationale Analysis, and Interpretability
Authors: Jason Wang, Xu Han, and Chloe Zheng
This repo replicates the Beer Advocate experiment as described in this paper.
Acknowledgements for this code include the original paper's source code and Adam Yala's PyTorch implemention.
To run the model, first you need to obtain the Beer Advocate review data by going here.
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Download the data, which are the files
reviews.aspect{#}.{dataset}.txt.gz, to"raw_data/beer_advocate/data". -
Download the embeddings, which is the file
review+wiki.filtered.200.txt.gz, to"raw_data/beer_advocate/embeddings".
Then run the model using an example command like so:
python -m main --train --test --get_rationales --aspect appearance --epochs 1 --cuda --num_workers 1 --debug_mode
Use --get_rationales to enable extractive rationales.
The results and extracted rationales will be saved in "results_path/args_dict.pkl" and be accessed as
results = pickle.load(open('results_path/args_dict.pkl','rb'))
rationales = results['test_stats']['rationales']
extracted_rationales.ipynb provides an example on a model that was trained with --debug_mode and 10 epochs.