How to Construct Deep Recurrent Neural Networks (1312.6026v5)

Abstract: In this paper, we explore different ways to extend a recurrent neural network (RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN which may be made deeper; (1) input-to-hidden function, (2) hidden-to-hidden transition and (3) hidden-to-output function. Based on this observation, we propose two novel architectures of a deep RNN which are orthogonal to an earlier attempt of stacking multiple recurrent layers to build a deep RNN (Schmidhuber, 1992; El Hihi and Bengio, 1996). We provide an alternative interpretation of these deep RNNs using a novel framework based on neural operators. The proposed deep RNNs are empirically evaluated on the tasks of polyphonic music prediction and LLMing. The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conventional, shallow RNNs.

• The paper introduces deeper RNN architectures by deepening input-to-hidden, hidden-to-hidden, and hidden-to-output functions for improved sequence modeling.
• It presents models like DT-RNN, DO-RNN, and DOT-RNN that outperform conventional RNNs in tasks such as polyphonic music prediction and language modeling.
• The study addresses training challenges with shortcut connections and sets the stage for future advancements in deep recurrent network design.

An Analysis of "How to Construct Deep Recurrent Neural Networks"

This paper by Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio offers a thorough exploration of deepening recurrent neural network (RNN) architectures to improve their performance on sequence modeling tasks. The authors dissect the concept of depth in RNNs, proposing architectures designed to exploit deep learning principles within recurrent models.

Introduction and Motivation

RNNs are recognized for their capability to model variable-length sequences. They have shown significant success in various applications like LLMing, speech recognition, and online handwriting recognition. However, unlike feedforward models, the notion of depth in RNNs is ambiguous. The motivation behind this work stems from the hypothesis that deeper models can more efficiently represent complex functions—a concept well-documented in feedforward networks.

Dissecting Depth in RNNs

The authors carefully analyze the architecture of RNNs and identify three specific points that can be deepened:

1. Input-to-Hidden Function: The authors propose making the function that maps inputs to hidden states deeper, thus leveraging higher-level representations to simplify capturing temporal structures.
2. Hidden-to-Hidden Transition: The transition between consecutive hidden states is made deeper to allow for nonlinear transformations, addressing the inherent limitations of shallow transitions.
3. Hidden-to-Output Function: Making the hidden-to-output function deeper helps disentangle variations in hidden states, facilitating more accurate output predictions.

Proposed Architectures

Building on these insights, the paper introduces several novel architectures:

• Deep Transition RNN (DT-RNN): This model uses a multilayer perceptron (MLP) for the hidden-to-hidden state transition.
• Deep Output RNN (DO-RNN): Here, an MLP with intermediate layers is used between hidden states and model outputs.
• Deep Transition and Output RNN (DOT-RNN): Combines deep hidden-to-hidden transitions with deep hidden-to-output mappings.
• Stacked RNN (sRNN): Introduces multiple recurrent layers, encouraging varying timescales in hidden states.

The paper also acknowledges potential training difficulties with these deeper models and proposes using shortcut connections to alleviate gradient vanishing/exploding problems.

Neural Operators and Alternative Perspectives

A notable contribution is the proposal of a framework using predefined neural operators, which bring flexibility and modularity to constructing deep RNNs. This operator-based approach reimagines RNNs as compositions of more complex neural functions, offering a potentially rich vein for future research.

Experimental Evaluation

The empirical validation of these deep RNNs covers tasks like polyphonic music prediction and LLMing. The experiments reveal several key insights:

1. For polyphonic music prediction, deep RNNs generally outperform conventional RNNs, although no single model uniformly excels across all datasets.
2. On LLMing tasks, the DOT-RNN shows superior performance, achieving state-of-the-art results in word-level LLMing.

Polyphonic Music Prediction Results:

• Nottingham Dataset: DT(S)-RNN surpasses the conventional RNN with lower negative log-probabilities (3.206 vs. 3.225).
• JSB Chorales and MuseData: The results corroborate the advantages of deeper architectures, although the optimal architecture varies by dataset.

LLMing Results:

• Character-Level Modeling (Penn Treebank): DOT(S)-RNN achieves a bit-per-character (BPC) score of 1.386, superior to other models but still shy of the best scores achieved using more advanced setups (e.g., mRNN with Hessian-free optimization).
• Word-Level Modeling: DOT(S)-RNN achieves a perplexity of 107.5, outperforming both DT(S)-RNN and sRNN, and improving upon prior state-of-the-art results.

Conclusions and Future Work

The paper concludes that deepening specific aspects of RNNs significantly enhances their sequence modeling capabilities. The findings encourage further exploration of combining various deep RNN architectures, leveraging advances in neural network activations and regularization methods like dropout.

Implications and Future Research Directions:

• Practical Applications: These deeper recurrent architectures hold promise for improved performance in real-world applications, including speech and language tasks.
• Theoretical Advances: Understanding the training complexity and devising efficient algorithms and regularization techniques remain open challenges.
• Architectural Innovations: Combining deep transition, deep output, and stacked RNN attributes offers a fertile ground for future developments, promising versatile and robust sequence models.

In summary, this paper significantly advances our understanding of deep RNN architectures, providing a clear pathway for enhancing the expressiveness and performance of recurrent models in various sequence modeling tasks.