@inproceedings{moeed-etal-2020-evaluation,
title = "An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing",
author = "Moeed, Abdul and
Hagerer, Gerhard and
Dugar, Sumit and
Gupta, Sarthak and
Ghosh, Mainak and
Danner, Hannah and
Mitevski, Oliver and
Nawroth, Andreas and
Groh, Georg",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.172",
pages = "1376--1381",
abstract = "A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.</abstract>
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%0 Conference Proceedings
%T An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing
%A Moeed, Abdul
%A Hagerer, Gerhard
%A Dugar, Sumit
%A Gupta, Sarthak
%A Ghosh, Mainak
%A Danner, Hannah
%A Mitevski, Oliver
%A Nawroth, Andreas
%A Groh, Georg
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F moeed-etal-2020-evaluation
%X A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.
%U https://aclanthology.org/2020.lrec-1.172
%P 1376-1381
Markdown (Informal)
[An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing](https://aclanthology.org/2020.lrec-1.172) (Moeed et al., LREC 2020)
ACL
- Abdul Moeed, Gerhard Hagerer, Sumit Dugar, Sarthak Gupta, Mainak Ghosh, Hannah Danner, Oliver Mitevski, Andreas Nawroth, and Georg Groh. 2020. An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1376–1381, Marseille, France. European Language Resources Association.