Learning Longer-term Dependencies in RNNs with Auxiliary Losses (1803.00144v3)

Abstract: 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 16\,000, 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.

• The paper introduces an auxiliary loss that augments RNN memory to capture long-term dependencies.
• It demonstrates improved performance on tasks such as pixel-by-pixel image and document classification, surpassing competitive baselines.
• The method reduces computational cost by enabling truncated backpropagation through time, making long sequence training more feasible.

Learning Longer-term Dependencies in RNNs with Auxiliary Losses

This paper discusses a novel methodology aimed at enhancing the capability of recurrent neural networks (RNNs) to capture long-term dependencies in sequences, an area that has historically posed considerable challenges due to issues like vanishing gradients and memory constraints of backpropagation through time (BPTT). The authors introduce a method that incorporates an unsupervised auxiliary loss into the main objective function, which strategically forces RNNs to reconstruct past events or predict future events in sequences. This approach serves to make truncated BPTT feasible over long sequences, while simultaneously improving the efficacy of full BPTT.

Key Contributions and Results

The primary contribution of this work is the introduction of an auxiliary loss that extends the RNNs' predictive ability. This auxiliary loss effectively acts as a memory augmentation tool, allowing models to learn longer-term dependencies by minimizing the loss over randomly sampled sequence subsections. The experimental results validate the method's effectiveness across various tasks, including pixel-by-pixel image classification with sequences up to 16,000 elements, and a document classification benchmark. The results demonstrate that RNNs leveraging auxiliary losses surpass competitive baselines regarding both performance and resource efficiency. Notably, the RNNs outperform comparable models like the Transformer in certain long sequence scenarios.

Theoretical and Practical Implications

The introduction of auxiliary losses fundamentally enhances the optimization and regularization processes within RNN training. By strategically truncating gradients, the methodology reduces the computational cost while maintaining performance levels. The auxiliary mechanism essentially alleviates memory demands associated with long sequence training, permitting RNNs to be more adaptable to diverse sequence lengths in real-world applications such as natural language processing.

Practically, this technique suggests pathways to more efficient model training across datasets with extensive temporal or spatial dimensions—such as video data, lengthy text corpuses, or high-resolution images—without requiring excessive computational resources. On the theoretical front, the authors provide compelling evidence that unsupervised auxiliary losses offer robust benefits, irrespective of the sequence's length or nature; an advantage that could inspire further exploration into hybrid models combining structured auxiliary objectives with traditional supervised tasks.

Future Directions

The results underscore the potential for integrating auxiliary losses in various architectures beyond conventional RNNs, potentially inspiring adaptations that leverage similar methods for Transformers or other deep learning architectures. Research may explore optimized configurations of auxiliary losses or their interplay with attention mechanisms, potentially leading to enhanced models for tasks requiring comprehensive sequence processing.

Overall, the paper contributes a significant step towards overcoming the limitations associated with RNN training on lengthy sequences, laying groundwork for future innovations in deep learning model efficiency and effectiveness.