PABI

This is the code repository for the EMNLP paper Foreseeing the Benefits of Incidental Supervision. If you use this code for your work, please cite

@inproceedings{HZNR21,
    author = {Hangfeng He and Mingyuan Zhang and Qiang Ning and Dan Roth},
    title = {{Foreseeing the Benefits of Incidental Supervision}},
    booktitle = {Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
    year = {2021},
    url = "https://cogcomp.seas.upenn.edu/papers/HZNR21.pdf",
    funding = {LwLL, MURI, CwC},
}

Installing Dependencies

Use virtual environment tools (e.g miniconda) to install packages and run experiments
python>=3.6
pip install -r requirements.txt

Code Organization

The code is organized as follows:

• bpp.py (CWBPP algorithm for learning with various inductive signals)
• run_ner.py (BERT for NER)
• run_squad.py (BERT for QA)

Reproducing experiments

To reproduce the experiments for learning with various inductive signals:

sh run_experiments.sh

To reproduce the experiments for cross-domain signals:

sh run_xdomain_ner_experiments.sh
sh run_xdomain_qa_experiments.sh