An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing
Abdul Moeed, Gerhard Hagerer, Sumit Dugar, Sarthak Gupta, Mainak Ghosh, Hannah Danner, Oliver Mitevski, Andreas Nawroth, Georg Groh
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.- Anthology ID:
- 2020.lrec-1.172
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1376–1381
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.172
- DOI:
- Bibkey:
- Cite (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 Twelfth Language Resources and Evaluation Conference, pages 1376–1381, Marseille, France. European Language Resources Association.
- Cite (Informal):
- An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing (Moeed et al., LREC 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.172.pdf
Export citation
@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", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Twelfth 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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%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 %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Twelfth 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)
- An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing (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 Twelfth Language Resources and Evaluation Conference, pages 1376–1381, Marseille, France. European Language Resources Association.