@inproceedings{pereira-etal-2020-adversarial,
title = "Adversarial Training for Commonsense Inference",
author = "Pereira, Lis and
Liu, Xiaodong and
Cheng, Fei and
Asahara, Masayuki and
Kobayashi, Ichiro",
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-1.8",
doi = "10.18653/v1/2020.repl4nlp-1.8",
pages = "55--60",
abstract = "We apply small perturbations to word embeddings and minimize the resultant adversarial risk to regularize the model. We exploit a novel combination of two different approaches to estimate these perturbations: 1) using the true label and 2) using the model prediction. Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model boosts the fine-tuning performance of RoBERTa, achieving competitive results on multiple reading comprehension datasets that require commonsense inference.",
}
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%0 Conference Proceedings
%T Adversarial Training for Commonsense Inference
%A Pereira, Lis
%A Liu, Xiaodong
%A Cheng, Fei
%A Asahara, Masayuki
%A Kobayashi, Ichiro
%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 pereira-etal-2020-adversarial
%X We apply small perturbations to word embeddings and minimize the resultant adversarial risk to regularize the model. We exploit a novel combination of two different approaches to estimate these perturbations: 1) using the true label and 2) using the model prediction. Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model boosts the fine-tuning performance of RoBERTa, achieving competitive results on multiple reading comprehension datasets that require commonsense inference.
%R 10.18653/v1/2020.repl4nlp-1.8
%U https://aclanthology.org/2020.repl4nlp-1.8
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.8
%P 55-60
Markdown (Informal)
[Adversarial Training for Commonsense Inference](https://aclanthology.org/2020.repl4nlp-1.8) (Pereira et al., RepL4NLP 2020)
ACL
- Lis Pereira, Xiaodong Liu, Fei Cheng, Masayuki Asahara, and Ichiro Kobayashi. 2020. Adversarial Training for Commonsense Inference. In Proceedings of the 5th Workshop on Representation Learning for NLP, pages 55–60, Online. Association for Computational Linguistics.