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Final project for MIT 6.864, along with Nico Rakover and Ambika Krishnamachar

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#####Ben Schreck, Nico Rakover, Ambika Krishnamachar

Refining recipes based on user reviews.

The code consists of two main components: the language model and the recipe modifier.

Language Model

The code in language_model/ can be used to train an RNN-based language model as well as to score review segments.

Running language_model/ will score a hand-labeled evaluation set, plot an ROC curve and display the best F1 score along with the threshold used to achieve it.

Recipe Modifier

The code in recipe-modifier/ can be used to train a network that will, given a recipe and a refinement, score each index in the recipe with how likely the refinement refers to said index.

The model can be found at recipe-modifier/

Baselines computes some baseline scores for the task of index prediction given a refinement and a recipe. The baseline models use a simple word-count-vector representation for segments and a few basic distance metrics to find the best scoring index. We show the Top-1 and Top-3 error rates for two tasks:

  1. identification of index for a modification refinement
  2. identification of index for an insertion refinement

Note that this setup is simpler than the task our model tackles, since our network models both tasks (modification and insertion) simultaneously.


trained-models/ contains parameter files for some of our trained models.


Final project for MIT 6.864, along with Nico Rakover and Ambika Krishnamachar






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