Oral
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Trieu H Trinh · Andrew Dai · Thang Luong · Quoc Le

Fri Jul 13th 11:50 AM -- 12:00 PM @ Victoria

Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences. This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer. Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation.

Author Information

Trieu H Trinh (Google Brain)
Andrew Dai (Google Brain)

Andrew Dai was awarded an MA in Computer Science at the University of Cambridge before receiving a PhD in Informatics at the University of Edinburgh for text modeling with Bayesian nonparametrics. He then subsequently worked at Google in Mountain View, California in a range of teams including machine translation, Google Now and Google Ads. In 2014, he joined the Google Brain team where he has worked on text representations, semi-supervised learning, sequence models, adversarial training and deep learning on medical data.

Thang Luong (Google Brain)
Quoc Le (Google Brain)

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