Minimal Contrastive Editing (MiCE) 🐭

This repository contains code for our paper, Explaining NLP Models via Minimal Contrastive Editing (MiCE).

Citation

@inproceedings{Ross2020ExplainingNM,
    title = "Explaining NLP Models via Minimal Contrastive Editing (MiCE)",
    author = "Ross, Alexis  and Marasovi{\'c}, Ana  and Peters, Matthew E.",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2021",
    publisher = "Association for Computational Linguistics",
    url= "https://arxiv.org/abs/2012.13985",
}

Installation

1. Clone the repository.

   git clone https://github.com/allenai/mice.git
   cd mice

2. Download and install Conda.

3. Create a Conda environment.

   conda create -n mice python=3.7

4. Activate the environment.

   conda activate mice

5. Download the requirements.

   pip3 install -r requirements.txt

Quick Start

1. Download Task Data: If you want to work with the RACE dataset, download it here: Link. The commands below assume that this data, after downloaded, is stored in data/RACE/. All other task-specific datasets are automatically downloaded by the commands below.

2. Download Pretrained Models: You can download pretrained models by running:

   bash download_models.sh

   For each task (IMDB/Newsgroups/RACE), this script saves the:

   • Predictor model to: trained_predictors/{TASK}/model/model.tar.gz.
   • Editor checkpoint to: results/{TASK}/editors/mice/{TASK}_editor.pth.

3. Generate Edits: Run the following command to generate edits for a particular task with our pretrained editor. It will write edits to results/{TASK}/edits/{STAGE2EXP}/edits.csv.

    python run_stage_two.py -task {TASK} -stage2_exp {STAGE2EXP} -editor_path results/{TASK}/editors/mice/{TASK}_editor.pth
   

   For instance, to generate edits for the IMDB task, the following command will save edits to results/imdb/edits/mice_binary/edits.csv:

   python run_stage_two.py -task imdb -stage2_exp mice_binary -editor_path results/imdb/editors/mice/imdb_editor.pth

4. Inspect Edits: Inspect these edits with the demo notebook notebooks/evaluation.ipynb.

More Information

run_all.sh contains commands for recreating the main experiments in our paper.

Training Predictors

We use AllenNLP to train our Predictor models. Code for training Predictors can be found in src/predictors/. See run_all.sh for commands used to train Predictors, which will save models to subfolders in trained_predictors.

Alternatively, you can work with our pretrained models, which you can download with download_models.sh.

Training Editors

The following command will train an editor (i.e. run Stage 1 of MiCE) for a particular task. It saves checkpoints to results/{TASK}/editors/{STAGE1EXP}/checkpoints/.

python run_stage_one.py -task {TASK} -stage1_exp {STAGE1EXP}

Generating Edits

The following command will find MiCE edits (i.e. run Stage 2 of MiCE) for a particular task. It saves edits to results/{TASK}/edits/{STAGE2EXP}/edits.csv. -editor_path determines the Editor model to use. Defaults to our pretrained Editor.

python run_stage_two.py -task {TASK} -stage2_exp {STAGE2EXP} -editor_path results/{TASK}/editors/mice/{TASK}_editor.pth

Inspecting Edits

The notebook notebooks/evaluation.ipynb contains some code to inspect edits. To compute fluency of edits, see the EditEvaluator class in src/edit_finder.py.

Adding a Task

Follow the steps below to extend this repo for your own task.

1. Create a subfolder within src/predictors/{TASK}

2. Dataset reader: Create a task specific dataset reader in a file {TASK}_dataset_reader.py within that subfolder. It should have methods: text_to_instance(), _read(), and get_inputs().

3. Train Predictor: Create a training config (see src/predictors/imdb/imdb_roberta.json for an example). Then train the Predictor using AllenNLP (see above commands or commands in run_all.sh for examples).

4. Train Editor Model: Depending on the task, you may have to create a new StageOneDataset subclass (see RaceStageOneDataset in src/dataset.py for an example of how to inherit from StageOneDataset).

   • For classification tasks, the existing base StageOneDataset class should work.
   • For new multiple-choice QA tasks with dataset readers patterned after the RaceDatasetReader (src/predictors/race/race_dataset_reader.py), the existing RaceStageOneDataset class should work.

5. Generate Edits: Depending on the task, you may have to create a new Editor subclass (see RaceEditor in src/editor.py for an example of how to inherit from Editor).

   • For classification tasks, the existing base Editor class should work.
   • For multiple-choice QA with dataset readers patterned after RaceDatasetReader, the existing RaceEditor class should work.