Code for ACL-IJCNLP 2021 paper: "Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition"
This is the partially re-writted code based-on AllenNLP. I've reproduced the unsupervised setting results.
For the original experiment code and data, please refer to crowd-NER.
- GOLD:
python main.py --name=gold --train_file=ground_truth --config=annotator-agnostic
- ALL:
python main.py --name=all --train_file=answers --config=annotator-agnostic
- MV:
python main.py --name=mv --train_file=mv --config=annotator-agnostic
- Our model:
python main.py --name=pgn --train_file=answers --config=pgn
@inproceedings{zhang-etal-2021-crowdsourcing,
title = "Crowdsourcing Learning as Domain Adaptation: {A} Case Study on Named Entity Recognition",
author = "Zhang, Xin and
Xu, Guangwei and
Sun, Yueheng and
Zhang, Meishan and
Xie, Pengjun",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.432",
doi = "10.18653/v1/2021.acl-long.432",
pages = "5558--5570",
}