@inproceedings{vernikos-etal-2020-domain,
title = "{D}omain {A}dversarial {F}ine-{T}uning as an {E}ffective {R}egularizer",
author = "Vernikos, Giorgos and
Margatina, Katerina and
Chronopoulou, Alexandra and
Androutsopoulos, Ion",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.278",
doi = "10.18653/v1/2020.findings-emnlp.278",
pages = "3103--3112",
abstract = "In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. However, standard fine-tuning can degrade the general-domain representations captured during pretraining. To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer. Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective. This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations. Indomain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain. Intuitively, the adversarial classifier acts as a regularize which prevents the model from overfitting to the task-specific domain. Empirical results on various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning.",
}
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<abstract>In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. However, standard fine-tuning can degrade the general-domain representations captured during pretraining. To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer. Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective. This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations. Indomain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain. Intuitively, the adversarial classifier acts as a regularize which prevents the model from overfitting to the task-specific domain. Empirical results on various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning.</abstract>
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%0 Conference Proceedings
%T Domain Adversarial Fine-Tuning as an Effective Regularizer
%A Vernikos, Giorgos
%A Margatina, Katerina
%A Chronopoulou, Alexandra
%A Androutsopoulos, Ion
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F vernikos-etal-2020-domain
%X In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. However, standard fine-tuning can degrade the general-domain representations captured during pretraining. To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer. Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective. This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations. Indomain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain. Intuitively, the adversarial classifier acts as a regularize which prevents the model from overfitting to the task-specific domain. Empirical results on various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning.
%R 10.18653/v1/2020.findings-emnlp.278
%U https://aclanthology.org/2020.findings-emnlp.278
%U https://doi.org/10.18653/v1/2020.findings-emnlp.278
%P 3103-3112
Markdown (Informal)
[Domain Adversarial Fine-Tuning as an Effective Regularizer](https://aclanthology.org/2020.findings-emnlp.278) (Vernikos et al., Findings 2020)
ACL
- Giorgos Vernikos, Katerina Margatina, Alexandra Chronopoulou, and Ion Androutsopoulos. 2020. Domain Adversarial Fine-Tuning as an Effective Regularizer. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3103–3112, Online. Association for Computational Linguistics.