Recurrent Dropout without Memory Loss

Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth


Abstract
This paper presents a novel approach to recurrent neural network (RNN) regularization. Differently from the widely adopted dropout method, which is applied to forward connections of feedforward architectures or RNNs, we propose to drop neurons directly in recurrent connections in a way that does not cause loss of long-term memory. Our approach is as easy to implement and apply as the regular feed-forward dropout and we demonstrate its effectiveness for the most effective modern recurrent network – Long Short-Term Memory network. Our experiments on three NLP benchmarks show consistent improvements even when combined with conventional feed-forward dropout.
Anthology ID:
C16-1165
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1757–1766
Language:
URL:
https://www.aclweb.org/anthology/C16-1165
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-1165.pdf