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This is a quick and dirty Tensorflow implementation of an attention based NLP model that learns relationships between sentences. It is inspired by the paper: A Decomposable Attention Model for Natural Language Inference

The original paper has used the model on the Stanford Natural Language Inference (SNLI) dataset. I adapted this model to compete in Quora Question Pairs on Kaggle

There are three main files:

  1. pair_classifier_model.py defines a class that creates the model's graph. The paper has not described their model in complete detail. So it may differ in some ways, but I believe it captures the essence of what is described in the paper.

  2. pair_classifier_train.py loads and prepares training and validation datasets and iterates through question pairs one by one. It also saves model checkpoints in ./save directory, and produces a log on ./log for viewing in Tensorboard. No batching has been implemented.

  3. pair_classifier_infer.py loads and prepares test data for the competition. It then restores the model from the latest checkpoint created by pair_classifier_train.py and iterates through the test data. Finally, it creates two CSV files ready to be submitted to Kaggle.

For a detailed description of this project and the results, please see this post.

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Tensorflow implementation of Decomposable Attention Model

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