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
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1376–1381
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.172
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.lrec-1.172.pdf