This repo holds the code for our weak supervision framework, ASTRA, described in our NAACL 2021 paper: "Self-Training with Weak Supervision"

ASTRA is a weak supervision framework for training deep neural networks by automatically generating weakly-labeled data. Our framework can be used for tasks where it is expensive to manually collect large-scale labeled training data.

ASTRA leverages domain-specific rules, a large amount of unlabeled data, and a small amount of labeled data through a teacher-student architecture:

Main components:

• Weak Rules: domain-specific rules, expressed as Python labeling functions. Weak supervision usually considers multiple rules that rely on heuristics (e.g., regular expressions) for annotating text instances with weak labels.
• Student: a base model (e.g., a BERT-based classifier) that provides pseudo-labels as in standard self-training. In contrast to heuristic rules that cover a subset of the instances, the student can predict pseudo-labels for all instances.
• RAN Teacher: our Rule Attention Teacher Network that aggregates the predictions of multiple weak sources (rules and student) with instance-specific weights to compute a single pseudo-label for each instance.

The following table reports classification results over 6 benchmark datasets averaged over multiple runs.

Our NAACL'21 paper describes our ASTRA framework and more experimental results in detail.

First, create a conda environment running Python 3.6:

conda create --name astra python=3.6
conda activate astra

Then, install the required dependencies:

pip install -r requirements.txt

For reproducibility, you can directly download our pre-processed data files (split into multiple unlabeled/train/dev sets):

cd data
bash prepare_data.sh

The original datasets are available here.

To replicate our NAACL '21 experiments, you can directly run our bash script:

cd scripts
bash run_experiments.sh

The above script will run ASTRA and report results under a new "experiments" folder.

You can alternatively run ASTRA with custom arguments as:

cd astra
python main.py --dataset <DATASET> --student_name <STUDENT> --teacher_name <TEACHER>

Supported STUDENT models:

1. logreg: Bag-of-words Logistic Regression classifier
2. elmo: ELMO-based classifier
3. bert: BERT-based classifier

Supported TEACHER models:

1. ran: our Rule Attention Network (RAN)

We will soon add instructions for supporting custom datasets as well as student and teacher components.

@InProceedings{karamanolakis2021self-training,
author = {Karamanolakis, Giannis and Mukherjee, Subhabrata (Subho) and Zheng, Guoqing and Awadallah, Ahmed H.},
title = {Self-training with Weak Supervision},
booktitle = {NAACL 2021},
year = {2021},
month = {May},
publisher = {NAACL 2021},
url = {https://www.microsoft.com/en-us/research/publication/self-training-weak-supervision-astra/},
}

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