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

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

We will soon add detailed instructions for downloading datasets and domain-specific rules as well as supporting custom datasets.

ASTRA leverages domain-specific rules, a large amount of unlabeled data, and a small amount of labeled data via iterative self-training.

You can run ASTRA as:

cd astra
python main.py --dataset <DATASET> --student_name <STUDENT_MODEL> --teacher_name <TEACHER_MODEL>

Supported <STUDENT_MODEL> arguments:

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

Supported <TEACHER_MODEL> arguments:

1. ran: our Rule Attention Network (RAN)

We will soon add instructions for supporting custom 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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