Continuous Learning in a Hierarchical Multiscale Neural Network

Thomas Wolf, Julien Chaumond, Clement Delangue


Abstract
We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework. We propose a hierarchical multi-scale language model in which short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time-scale dependencies are encoded in the dynamic of the lower-level network by having a meta-learner update the weights of the lower-level neural network in an online meta-learning fashion. We use elastic weights consolidation as a higher-level to prevent catastrophic forgetting in our continuous learning framework.
Anthology ID:
P18-2001
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–7
Language:
URL:
https://aclanthology.org/P18-2001
DOI:
10.18653/v1/P18-2001
Bibkey:
Cite (ACL):
Thomas Wolf, Julien Chaumond, and Clement Delangue. 2018. Continuous Learning in a Hierarchical Multiscale Neural Network. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1–7, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Continuous Learning in a Hierarchical Multiscale Neural Network (Wolf et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P18-2001.pdf
Data
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