@inproceedings{chronopoulou-etal-2019-embarrassingly,
title = "An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models",
author = "Chronopoulou, Alexandra and
Baziotis, Christos and
Potamianos, Alexandros",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1213",
doi = "10.18653/v1/N19-1213",
pages = "2089--2095",
abstract = "A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora. In this paper we present a conceptually simple and effective transfer learning approach that addresses the problem of catastrophic forgetting. Specifically, we combine the task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. This preserves language regularities captured by language models, while enabling sufficient adaptation for solving the target task. Our method does not require pretraining or finetuning separate components of the network and we train our models end-to-end in a single step. We present results on a variety of challenging affective and text classification tasks, surpassing well established transfer learning methods with greater level of complexity.",
}
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%0 Conference Proceedings
%T An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
%A Chronopoulou, Alexandra
%A Baziotis, Christos
%A Potamianos, Alexandros
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F chronopoulou-etal-2019-embarrassingly
%X A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora. In this paper we present a conceptually simple and effective transfer learning approach that addresses the problem of catastrophic forgetting. Specifically, we combine the task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. This preserves language regularities captured by language models, while enabling sufficient adaptation for solving the target task. Our method does not require pretraining or finetuning separate components of the network and we train our models end-to-end in a single step. We present results on a variety of challenging affective and text classification tasks, surpassing well established transfer learning methods with greater level of complexity.
%R 10.18653/v1/N19-1213
%U https://aclanthology.org/N19-1213
%U https://doi.org/10.18653/v1/N19-1213
%P 2089-2095
Markdown (Informal)
[An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models](https://aclanthology.org/N19-1213) (Chronopoulou et al., NAACL 2019)
ACL