Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture

Christopher Brix, Parnia Bahar, Hermann Ney


Abstract
Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computations using neural networks. The stabilized lottery ticket hypothesis states that networks can be pruned after none or few training iterations, using a mask computed based on the unpruned converged model. On the transformer architecture and the WMT 2014 English-to-German and English-to-French tasks, we show that stabilized lottery ticket pruning performs similar to magnitude pruning for sparsity levels of up to 85%, and propose a new combination of pruning techniques that outperforms all other techniques for even higher levels of sparsity. Furthermore, we confirm that the parameter’s initial sign and not its specific value is the primary factor for successful training, and show that magnitude pruning cannot be used to find winning lottery tickets.
Anthology ID:
2020.acl-main.360
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3909–3915
URL:
https://www.aclweb.org/anthology/2020.acl-main.360
DOI:
10.18653/v1/2020.acl-main.360
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PDF:
https://www.aclweb.org/anthology/2020.acl-main.360.pdf