This is the Github repository of our paper, "RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models" (in Proc. of EMNLP2021).
✍️ Bill Yuchen Lin, Wenyang Gao, Jun Yan, Ryan Moreno, Xiang Ren
🏢 in Proceedings of EMNLP 2021 (short)
🌐 Project website: https://inklab.usc.edu/rockner/.
To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples. Specifically, at the entity level, we replace target entities with other entities of the same semantic class in Wikidata; at the context level, we use pre-trained language models (e.g., BERT) to generate word substitutions. Together, the two levels of at- tack produce natural adversarial examples that result in a shifted distribution from the training data on which our target models have been trained. We apply the proposed method to the OntoNotes dataset and create a new benchmark named OntoRock for evaluating the robustness of existing NER models via a systematic evaluation protocol. Our experiments and analysis reveal that even the best model has a significant performance drop, and these models seem to memorize in-domain entity patterns instead of reasoning from the context. Our work also studies the effects of a few simple data augmentation methods to improve the robustness of NER models.
{: style="border: 0px solid black"}
Please download our OntoRock dataset by filling the form here and the link will show up once you read the disclaimer and submit it. There are eight files as follows:
Original-OntoNotes_train.txt
(1,148,427 lines)- The original training data of OntoNotes.
OntoRock-Full_dev.txt
(161,123 lines)- The development data of OntoRock-Full.
OntoRock-Entity_dev.txt
(161,152 lines)- The development data of OntoRock-Entity.
OntoRock-Context_dev.txt
(156,215 lines)- The development data of OntoRock-Context.
Original-OntoNotes_test_pub.txt
(160,989 lines)- The original test data of OntoNotes, where the truth tags are hidden.
OntoRock-Full_test_pub.txt
(165,872 lines)- The test data of OntoRock-Full, where the truth tags are hidden.
OntoRock-Entity_test_pub.txt
(165,906 lines)- The test data of OntoRock-Entity, where the truth tags are hidden.
OntoRock-Context_test_pub.txt
(160,953 lines)- The test data of OntoRock-Context, where the truth tags are hidden.
# a sentence in our txt file, truth tags are hidden in test files
We O
respectfully O
invite O
you O
to O
watch O
a O
special O
edition O
of O
Across B-ORG
China I-ORG
. O
# sentences are separated by blank line
This repo is now under active development, and there may be issues caused by refactoring code. Please email yuchen.lin@usc.edu if you have any questions.
@inproceedings{lin-etal-2021-rockner,
title = "RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models",
author = "Lin, Bill Yuchen and Gao, Wenyang and Yan, Jun and Moreno, Ryan and Ren, Xiang",
booktitle = "Proc. of EMNLP (short paper)",
year = "2021",
note={to appear}
}