Hierarchical Multiscale Recurrent Neural Networks (1609.01704v7)

Abstract: Learning both hierarchical and temporal representation has been among the long-standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchical structure of the sequence. In this paper, we propose a novel multiscale approach, called the hierarchical multiscale recurrent neural networks, which can capture the latent hierarchical structure in the sequence by encoding the temporal dependencies with different timescales using a novel update mechanism. We show some evidence that our proposed multiscale architecture can discover underlying hierarchical structure in the sequences without using explicit boundary information. We evaluate our proposed model on character-level LLMling and handwriting sequence modelling.

• The paper introduces a dynamic boundary detection mechanism that uses UPDATE, COPY, and FLUSH operations to efficiently capture latent hierarchical structures in sequences.
• The novel update mechanism minimizes computational overhead and alleviates vanishing gradients, achieving impressive results on character-level and handwriting datasets.
• Empirical evaluations demonstrate the model’s versatility and practical impact for applications in language modeling and sequence generation tasks.

Hierarchical Multiscale Recurrent Neural Networks

The paper presents a sophisticated approach to modeling temporal data through hierarchical multiscale recurrent neural networks (HM-RNNs). This research addresses the complex issue of learning both hierarchical and temporal representations within recurrent neural networks, a challenge that has persisted despite advancements in the field.

Key Contributions

1. Hierarchical Boundary Detection: The paper introduces a hierarchical multiscale RNN capable of dynamically identifying and learning from latent hierarchical structures in sequences without requiring explicit boundary information. This innovation contrasts with previous models which relied on manually set timescale hyperparameters.
2. Novel Update Mechanism: The proposed architecture employs three distinct operations—UPDATE, COPY, and FLUSH—facilitating efficient learning of hierarchical multiscale structures. This mechanism optimizes for computational efficiency and mitigates issues such as the vanishing gradient problem by reducing updates at higher layers.
3. Boundary Detector Using Binary Variables: Each layer includes a boundary detector that learns to detect segment end points—a significant enhancement over fixed-rate hierarchical RNNs and Clockwork RNNs. The use of a straight-through estimator for training ensures efficient gradient computation despite the non-differentiability of boundary decisions.

Empirical Evaluation

The HM-RNN model demonstrates its efficacy across multiple datasets and tasks:

• Character-Level LLMing: The model achieves state-of-the-art results on the Text8 dataset while offering comparable performance on the Penn Treebank and Hutter Prize Wikipedia datasets. These outcomes underline the model's ability to capture and utilize hierarchical structures effectively in discrete sequence data.
• Handwriting Sequence Generation: The application of HM-RNNs to real-valued sequences, such as those in handwriting data, further showcases the model's versatility. The empirical results indicate improved performance over traditional LSTMs, highlighting the model’s adaptability to sequences with clear hierarchical segmentations.

Implications and Future Directions

The research offers both theoretical and practical implications. Theoretically, it opens avenues for developing RNNs that can autonomously discover and exploit latent structures in data, a significant departure from the need for explicit annotations or fixed temporal scales. Practically, the model’s architecture is likely to influence areas such as speech recognition, machine translation, and any domain that benefits from hierarchical sequence understanding.

Future research directions could explore the extension of this approach to other forms of neural networks or its integration with attention mechanisms. Further optimizations in boundary detection and segment processing could enhance the model's performance and interpretability.

In conclusion, the HM-RNN approach presents a compelling framework for modeling sequences with intrinsic hierarchical structures, offering both novel insights and strong empirical performance in sequence modeling tasks.