Multiplicative LSTM for sequence modelling

Ben Krause, Iain Murray, Steve Renals, Liang Lu

TL;DR: Combines LSTM and multiplicative RNN architectures; achieves 1.19 bits/character on Hutter prize dataset with dynamic evaluation.

Abstract:

We introduce multiplicative LSTM (mLSTM), a novel recurrent neural network
architecture for sequence modelling that combines the long short-term memory
(LSTM) and multiplicative recurrent neural network architectures. mLSTM is
characterised by its ability to have different recurrent transition functions for each
possible input, which we argue makes it more expressive for autoregressive density
estimation. We demonstrate empirically that mLSTM outperforms standard LSTM
and its deep variants for a range of character level modelling tasks, and that this
improvement increases with the complexity of the task. This model achieves a
test error of 1.19 bits/character on the last 4 million characters of the Hutter prize
dataset when combined with dynamic evaluation.

Keywords: Deep learning, Unsupervised Learning, Natural language processing

Conflicts: ed.ac.uk, ttic.edu

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:1609.07959/code)