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.
| Method | TREC | SMS | YouTube | CENSUS | MIT-R | Spouse |
|---|---|---|---|---|---|---|
| Majority Voting | 60.9 | 48.4 | 82.2 | 80.1 | 40.9 | 44.2 |
| Snorkel | 65.3 | 94.7 | 93.5 | 79.1 | 75.6 | 49.2 |
| Classic Self-training | 71.1 | 95.1 | 92.5 | 78.6 | 72.3 | 51.4 |
| ASTRA | 80.3 | 95.3 | 95.3 | 83.1 | 76.1 | 62.3 |
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:
- logreg: Bag-of-words Logistic Regression classifier
- elmo: ELMO-based classifier
- bert: BERT-based classifier
Supported TEACHER models:
- 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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