@inproceedings{balasubramanian-etal-2020-whats,
title = "What{'}s in a Name? Are {BERT} Named Entity Representations just as Good for any other Name?",
author = "Balasubramanian, Sriram and
Jain, Naman and
Jindal, Gaurav and
Awasthi, Abhijeet and
Sarawagi, Sunita",
editor = "Gella, Spandana and
Welbl, Johannes and
Rei, Marek and
Petroni, Fabio and
Lewis, Patrick and
Strubell, Emma and
Seo, Minjoon and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.24",
doi = "10.18653/v1/2020.repl4nlp-1.24",
pages = "205--214",
abstract = "We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input. We highlight that on several tasks while such perturbations are natural, state of the art trained models are surprisingly brittle. The brittleness continues even with the recent entity-aware BERT models. We also try to discern the cause of this non-robustness, considering factors such as tokenization and frequency of occurrence. Then we provide a simple method that ensembles predictions from multiple replacements while jointly modeling the uncertainty of type annotations and label predictions. Experiments on three NLP tasks shows that our method enhances robustness and increases accuracy on both natural and adversarial datasets.",
}
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<abstract>We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input. We highlight that on several tasks while such perturbations are natural, state of the art trained models are surprisingly brittle. The brittleness continues even with the recent entity-aware BERT models. We also try to discern the cause of this non-robustness, considering factors such as tokenization and frequency of occurrence. Then we provide a simple method that ensembles predictions from multiple replacements while jointly modeling the uncertainty of type annotations and label predictions. Experiments on three NLP tasks shows that our method enhances robustness and increases accuracy on both natural and adversarial datasets.</abstract>
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%0 Conference Proceedings
%T What’s in a Name? Are BERT Named Entity Representations just as Good for any other Name?
%A Balasubramanian, Sriram
%A Jain, Naman
%A Jindal, Gaurav
%A Awasthi, Abhijeet
%A Sarawagi, Sunita
%Y Gella, Spandana
%Y Welbl, Johannes
%Y Rei, Marek
%Y Petroni, Fabio
%Y Lewis, Patrick
%Y Strubell, Emma
%Y Seo, Minjoon
%Y Hajishirzi, Hannaneh
%S Proceedings of the 5th Workshop on Representation Learning for NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F balasubramanian-etal-2020-whats
%X We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input. We highlight that on several tasks while such perturbations are natural, state of the art trained models are surprisingly brittle. The brittleness continues even with the recent entity-aware BERT models. We also try to discern the cause of this non-robustness, considering factors such as tokenization and frequency of occurrence. Then we provide a simple method that ensembles predictions from multiple replacements while jointly modeling the uncertainty of type annotations and label predictions. Experiments on three NLP tasks shows that our method enhances robustness and increases accuracy on both natural and adversarial datasets.
%R 10.18653/v1/2020.repl4nlp-1.24
%U https://aclanthology.org/2020.repl4nlp-1.24
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.24
%P 205-214
Markdown (Informal)
[What’s in a Name? Are BERT Named Entity Representations just as Good for any other Name?](https://aclanthology.org/2020.repl4nlp-1.24) (Balasubramanian et al., RepL4NLP 2020)
ACL