Interpretable Neural Predictions with Differentiable Binary Variables

This is the repository for Interpretable Neural Predictions with Differentiable Binary Variables, accepted at ACL 2019. The models in this repository learn to select a rationale, a portion of the input text, serving as an explanation of what is important for classification. Rationales are trained jointly with classification, either with REINFORCE (for the Lei et al. method), or simply using backpropagation (for our method using the HardKuma distribution).

If you use this code in your work, then please cite our paper [bib]

Instructions

Installation

You need to have Python 3.6 or higher installed. It is recommended that you use a virtual environment:

sudo pip3 install -U virtualenv
virtualenv --system-site-packages -p python3 ./my_venv
source ./my_venv/bin/activate

Install all required Python packages using:

pip install -r requirements.txt

Clone the repository:

git clone https://github.com/bastings/interpretable_predictions.git

And finally download the data:

cd interpretable_predictions
./download_data.sh

Tensorboard

You can folllow training progress for all experiments using tensorboard. Just point it to the directory of your experiment and open your browser:

tensorboard --logdir YOUR_EXPERIMENT_DIRECTORY --port 6006

Multi-aspect Sentiment Analysis (Beer Advocate)

See beer directory.

There are three models that you can choose from using --model:

1. baseline (just classify the training instances)
2. rl (the bernoulli baseline / Lei et al. reimplementation)
3. latent (hardkuma)

To train a Bernoulli/RL model on a single aspect (as in Table 2), e.g. aspect 0 (look):

python -m latent_rationale.beer.train \
    --model rl \
    --aspect 0 \
    --train_path data/beer/reviews.aspect0.train.txt.gz \
    --dev_path data/beer/reviews.aspect0.heldout.txt.gz \
    --scheduler multistep \
    --save_path results/beer/rl_a0 \
    --dependent-z \
    --sparsity 0.0003 --coherence 2

To train a latent/HardKuma model on a single aspect (as in Table 2), e.g. aspect 0 (look):

python -m latent_rationale.beer.train \
    --model latent \
    --aspect 0 \
    --train_path data/beer/reviews.aspect0.train.txt.gz \
    --dev_path data/beer/reviews.aspect0.heldout.txt.gz \
    --scheduler exponential \
    --save_path results/beer/latent_a0 \
    --dependent-z \
    --selection 0.13 --lasso 0.02

To train a baseline on all aspects:

python -m latent_rationale.beer.train \
    --model baseline \
    --aspect -1 \
    --train_path data/beer/reviews.260k.train.txt.gz \
    --dev_path data/beer/reviews.260k.heldout.txt.gz \
    --save_path results/beer/baseline_multi

For help/more options:

python -m latent_rationale.beer.train -h

To predict:

python -m latent_rationale.beer.predict --ckpt path/to/model/directory

Stanford Sentiment (SST)

To train the latent rationale model to select 30% of text:

python -m latent_rationale.sst.train \
  --model latent --selection 0.3 --save_path results/sst/latent_30pct

To train the Bernoulli (REINFORCE) model with L0 penalty weight 0.01:

python -m latent_rationale.sst.train \
  --model rl --sparsity 0.01 --save_path results/sst/bernoulli_sparsity01

SNLI

To train our reimplemenation of Decomposable Attention (our baseline):

python -m latent_rationale.snli.train --save_path results/snli/da --model decomposable

(You can enable the self-attention option using --self-attention.)

To train the latent rationale model to select 10% of attention cells:

python -m latent_rationale.snli.train --save_path results/snli/latent_10pct --model decomposable --dist hardkuma --selection 0.10

Lastly there is a recurrent baseline model as well:

python -m latent_rationale.snli.train --save_path results/snli/rnn --model recurrent

Notebooks

We curate an updated HardKuma implementation at https://github.com/probabll/sparse-distributions. You can also find other distributions there.