TestAug - A Framework for Augmenting Capability-based NLP Tests

Pipeline

The following will demonstrate how TestAug is applied to testing a sentiment classifier's ability to handle negation in a sentence. Before going through each step, make sure that

1. The computation environment is properly set up.

2. You have a valid access key to GPT-3 and it could be accessed from ~/.bashrc as something look like below. You could still experience our system without this key by going to Step 5 directly as a sample of the queried dataset is provided.

   export OPENAI_API_KEY="XYZ"

Now we are ready to use the TestAug system:

• Step 0: Clone the repository and make sure the paths in the setting/setting.py are set correctly.

• Step 1: Query and annotate the sentences based on seed sentences from the CheckList test suite.

  • The --description 3 specifies one concrete case of negation in the CheckList test suite:

    A negative sentiment sentence with negated positive sentiment word and neutral contents in the middle.

  • The queried and annotated sentences will appear in the following directories:

    • Unlabeled Data: dataset/unlabeled/003/unlabeled@<timestamp>.pkl
    • Labeled Data: dataset/labeling/003/<timestamp>.json.

  python pipeline/01_annotate.py --query --annotate --task sentiment --description 3

• Step 2: Inspect the labeled data in data/labeling/003/ to see if the ratio of valid sentences $\tau$ is above the predefined threshold (in our project, this threshold is set to 90%).

  • If $\tau \geq 90%$, directly proceed to Step 4 without stepping into Step 3.
  • if $\tau < 90%$, repeat Step 1 as follows, where --phase2 will query a training set to train a roberta-base classifier to filter out the invalid sentences. Proceed to Step 3 after this step.

  In our case, we do need to go through this additional step as the ratio is below 90%.

  python pipeline/01_annotate.py --query --annotate --task sentiment --description 3 --phase2

• Step 3: Train a roberta-base classifier to automatically filter out the invalid sentences.

  # prepare training data
  python pipeline/02_prepare_data.py --task sentiment --description 3 --save
  
  # train the classifier
  python pipeline/03_classify.py --task sentiment --description 3 --train --test

• Step 4: Query a set of test cases and filter invalid ones out if necessary

  python pipeline/04_query.py --task sentiment --description 3

• Step 5: Filter out the invalid cases if the ratio of valid cases exceeds the predefined threshold (Step 2).

  python pipeline/05_filter.py --task sentiment

• Step 6: Test the classifiers in question with the augmented test suite provided by TestAug. Note that --seed 42 here is related to the test suites evaluation mentioned in the paper; it could be set to a different value for a more complete comparison.

  python pipeline/06_test.py --task sentiment --seed 42

  By default, the TestAug tests the following 4 models; all of them have shown decent accuracies on the original validation set. Additional models could be easily incorporated if they appear on the HuggingFace model hub.

  Model	Validation (Test) Accuracy
  textattack/distilbert-base-cased-SST-2	90.02%
  textattack/albert-base-v2-SST-2	92.55%
  textattack/bert-base-uncased-SST-2	92.43%
  textattack/roberta-base-SST-2	94.04%

• Step 6: Compare the model's error rates on different test suites.

  python pipeline/07_report.py --setting aggregate --task sentiment

Reproducing Experiments

With all three tasks' test suites available, Table 3 could be reproduced following the steps below.

• Step 1: Run the reproduce.sh script below:

  bash reproduce.sh

• Step 2: Reproduce Table 3 of the paper:

  python pipeline/07_report.py --task sentiment --table

Environment

It is recommended to set up our system using a computing platform with GPU support.

conda create --name testaug python==3.8.0
conda activate testaug

# generic libraries
conda install numpy pandas matplotlib seaborn scikit-learn ipython tqdm termcolor
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch 

# libraries related to NLP models
pip install transformers datasets
pip install simpletransformers

pip install -U pip setuptools wheel
pip install -U spacy
python -m spacy download en_core_web_sm

# libraries related to capability-based testing
pip install checklist

# library related to querying GPT-3
pip install openai
pip install retelimit

# library related to evaluation metrics
pip install networkx
pip install fast_bleu

# current project
pip install -e .