Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists

Giancarlo Salton, John Kelleher


Abstract
Recurrent Neural Network Language Models composed of LSTM units, especially those augmented with an external memory, have achieved state-of-the-art results in Language Modeling. However, these models still struggle to process long sequences which are more likely to contain long-distance dependencies because of information fading. In this paper we demonstrate an effective mechanism for retrieving information in a memory augmented LSTM LM based on attending to information in memory in proportion to the number of timesteps the LSTM gating mechanism persisted the information.
Anthology ID:
R19-1121
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1052–1059
Language:
URL:
https://www.aclweb.org/anthology/R19-1121
DOI:
10.26615/978-954-452-056-4_121
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PDF:
http://aclanthology.lst.uni-saarland.de/R19-1121.pdf