Gated Feedback Recurrent Neural Networks (1502.02367v4)

Abstract: In this work, we propose a novel recurrent neural network (RNN) architecture. The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of stacking multiple recurrent layers by allowing and controlling signals flowing from upper recurrent layers to lower layers using a global gating unit for each pair of layers. The recurrent signals exchanged between layers are gated adaptively based on the previous hidden states and the current input. We evaluated the proposed GF-RNN with different types of recurrent units, such as tanh, long short-term memory and gated recurrent units, on the tasks of character-level LLMing and Python program evaluation. Our empirical evaluation of different RNN units, revealed that in both tasks, the GF-RNN outperforms the conventional approaches to build deep stacked RNNs. We suggest that the improvement arises because the GF-RNN can adaptively assign different layers to different timescales and layer-to-layer interactions (including the top-down ones which are not usually present in a stacked RNN) by learning to gate these interactions.

• The paper introduces the GF-RNN architecture with adaptive gated-feedback connections between layers.
• Empirical evaluations show improved performance in character-level language modeling, achieving a BPC of 1.58 with LSTM units.
• GF-RNN outperforms traditional stacked RNNs in Python program evaluation by effectively managing complex, multiscale dependencies.

Gated Feedback Recurrent Neural Networks: An Essay

The paper "Gated Feedback Recurrent Neural Networks" by Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio proposes an innovative recurrent neural network (RNN) architecture known as the Gated Feedback RNN (GF-RNN). This essay provides a summary and analysis of this work for an audience of experienced researchers.

Introduction and Context

RNNs are extensively utilized for sequence modeling tasks due to their ability to handle sequences of varying lengths. Traditional RNN architectures, while theoretically capable of capturing long-term dependencies, often struggle with this in practice due to issues like vanishing gradients. Solutions such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures have been proposed to address these problems by incorporating gating mechanisms, thus enabling better memory retention over long sequences.

Proposed Method: Gated Feedback RNN

The GF-RNN extends the traditional approach of stacking multiple recurrent layers. It introduces a key innovation: gated-feedback connections that allow and control signals flowing both top-down and bottom-up between layers. Each pair of layers is interconnected through global gating units that adaptively gate the recurrent signals based on the current input and previous hidden states. This structure not only facilitates the flow of information at multiple timescales across layers but also enhances the adaptability of each layer.

Empirical Evaluation

The authors evaluated the GF-RNN using three types of recurrent units—$\tanh$, LSTM, and GRU—across two tasks: character-level LLMing and Python program evaluation.

1. Character-Level LLMing:
   • Utilizing the dataset from the human knowledge compression contest, the GF-RNN demonstrated superior performance compared to single-layer and conventionally stacked RNNs.
   • Specifically, the GF-RNN models with GRU and LSTM units showed improved bits-per-character (BPC) scores, outperforming the established methods.
   • In a comparative experiment involving a large GF-RNN with LSTM units, the model achieved a BPC of 1.58, surpassing the previously best-reported results.
2. Python Program Evaluation:
   • GF-RNNs were evaluated on their ability to predict the output of Python scripts, showcasing better performance as the complexity of the tasks increased.
   • The GF-RNNs, particularly with GRU and LSTM units, outperformed stacked RNNs across varying levels of nesting and target sequence lengths.
   • Heatmaps of test accuracies indicated that the GF-RNN models maintained superior accuracy, especially for more complex sequences.

Implications and Future Work

The introduction of gated-feedback mechanisms in RNNs represents a significant step in enhancing the ability of these networks to handle multiscale dependencies within sequences. This research potentially paves the way for more robust and adaptable sequence modeling architectures, which are crucial for tasks involving hierarchical or multi-timescale dependencies.

Future developments could explore further optimization of the gating mechanisms and examine their applicability across a broader range of sequence modeling tasks. Additionally, the deterioration in performance observed when combining GF-RNNs with $\tanh$ units calls for a deeper investigation into the interaction between different activation functions and the global gating mechanism.

Conclusion

The GF-RNN architecture proposed by Chung et al. offers a promising enhancement to traditional RNN structures, providing controlled, adaptive gating of inter-layer signals. This approach not only improves performance on complex sequence modeling tasks but also facilitates faster and more efficient learning. The empirical results demonstrate its efficacy, particularly when integrated with sophisticated gating units like LSTM and GRU, highlighting the architecture's potential for further advancements in the domain of recurrent neural networks.