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rationale

README.md

Learning Rationales behind Predictions

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This directory contains the code and resources of the following paper:

"Rationalizing Neural Predictions". Tao Lei, Regina Barzilay and Tommi Jaakkola. EMNLP 2016. [PDF] [Slides]

The method learns to provide justifications, i.e. rationales, as supporting evidence of neural networks' prediction. The following figure illustrates the rationales and the associated predictions for multi-aspect sentiment analysis on product reivew:


### Overview of the Model We optimize two modular (neural) components, generator and encoder, to produce rationales and predictions. The framework is generic -- generator and encoder can be implemented and realized in various ways such as using RNNs or CNNs. We train the model in a RL style using policy gradient (specifically REINFORCE), as illustrated below.


### Sub-directories - this root directory contains impelmentation of the rationale model used for the beer review data. ``rationale.py`` implements the independent selection version and ``rationale_dependent.py`` implements the sequential selection version. See the paper for details. - [example_rationales](example_rationales) contains rationales generated for the beer review data. - [ubuntu](ubuntu) contains alternative implementation for the AskUbuntu data. - [medical](medical) contains alternative implementation for medical report classification.
### Data - **Proudct reviews:** We provide subsets of reviews and pre-trained word embeddings at [here](http://people.csail.mit.edu/taolei/beer/). This should be sufficient for producing our results. Please contact the author of the dataset, [Prof. McAuley](http://cseweb.ucsd.edu/~jmcauley/) for the full set (1.5 million reviews). - **AskUbuntu data:** AskUbuntu question data is available in [this repo](https://github.com/taolei87/askubuntu). - **Pathology data:** This data is not available due to patients' privacy. We only provide the code and example snapshot at [/medical directory](medical)

Important Note: all data is for research-purpose only.


### Code Usage

To run the code, you need Numpy and Theano (> 0.7.0.dev-8d3a67 I used) installed. Next:

  1. Clone the rcnn repo
  2. Use “export PYTHONPATH=/path/to/rcnn/code” to add the rcnn/code directory to Python library
  3. Run python rationale.py --help or python rationale_dependent.py --help to see all running options

Example run of beer review data:

THEANO_FLAGS='device=gpu,floatX=float32'        # use GPU and 32-bit float
python rationale.py                             # independent selection version
      --embedding /path/to/vectors              # path to load word vectors (required)
      --train reviews.aspect0.train.txt.gz      # path to training set (required)
      --dev reviews.aspect0.heldout.txt.gz      # path to development set (required)        
      --load_rationale annotations.json         # path to rationale annotation for testing (required)
      --aspect 0                                # which aspect (-1 means all aspects)
      --dump outputs.json                       # dump selected rationales and predictions
      --sparsity 0.0003 --coherent 2.0          # regularizations

### To-do - [ ] better documentation of the code - [ ] more example usage of the code - [ ] put trained models in the repo??