3,849 research outputs found
Revisiting the Hierarchical Multiscale LSTM
Hierarchical Multiscale LSTM (Chung et al., 2016a) is a state-of-the-art
language model that learns interpretable structure from character-level input.
Such models can provide fertile ground for (cognitive) computational
linguistics studies. However, the high complexity of the architecture, training
procedure and implementations might hinder its applicability. We provide a
detailed reproduction and ablation study of the architecture, shedding light on
some of the potential caveats of re-purposing complex deep-learning
architectures. We further show that simplifying certain aspects of the
architecture can in fact improve its performance. We also investigate the
linguistic units (segments) learned by various levels of the model, and argue
that their quality does not correlate with the overall performance of the model
on language modeling.Comment: To appear in COLING 2018 (reproduction track
Simple Recurrent Units for Highly Parallelizable Recurrence
Common recurrent neural architectures scale poorly due to the intrinsic
difficulty in parallelizing their state computations. In this work, we propose
the Simple Recurrent Unit (SRU), a light recurrent unit that balances model
capacity and scalability. SRU is designed to provide expressive recurrence,
enable highly parallelized implementation, and comes with careful
initialization to facilitate training of deep models. We demonstrate the
effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over
cuDNN-optimized LSTM on classification and question answering datasets, and
delivers stronger results than LSTM and convolutional models. We also obtain an
average of 0.7 BLEU improvement over the Transformer model on translation by
incorporating SRU into the architecture.Comment: EMNL
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