Minimal Gated Unit for Recurrent Neural Networks (1603.09420v1)

Abstract: Recently recurrent neural networks (RNN) has been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN is a difficult task, partly because there are many competing and complex hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN's applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU's properties theoretically and empirically.

• The paper introduces MGU, a simplified recurrent unit that uses a single gate to maintain long-term dependencies.
• The methodology reduces parameter count by roughly one-third compared to GRU, resulting in faster training and lower computational demands.
• Experimental evaluations across various tasks demonstrate that MGU achieves comparable accuracy to GRU while enhancing efficiency.

Minimal Gated Unit for Recurrent Neural Networks

Recurrent neural networks (RNNs) have demonstrated exceptional utility in processing sequence data across a range of applications, from language translation to video action recognition. Yet, the quest for an optimal architecture remains unenviably complex, largely owing to the intricacy of popular hidden units like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). This paper proposes a notable simplification, the Minimal Gated Unit (MGU), which is designed with a single gate, ostensibly reducing the complexity inherent to traditional models while preserving computational efficacy and accuracy.

Key Contributions

The authors position MGU as a streamlined alternative to the LSTM and GRU by retaining the essential functionality with only one gate. This minimalistic approach is informed by prior evaluations and empirical analyses, which suggest that more gates do not necessarily correlate with improved performance. The proposed MGU integrates insights from recent studies, decisively leveraging the forget gate, which has been identified as pivotal for maintaining long-term dependencies in sequence data.

Methodological Design

The MGU architecture is essentially a pared-down version of the GRU, differing by employing a single gating mechanism analogous to the forget gate in LSTMs. The proposed architecture thereby reduces the parameter count significantly—by approximately one-third compared to the GRU. The design simplification translates to faster training times and reduced computational demands, without sacrificing the RNN's ability to model complex sequences.

Experimental Evaluation

The MGU's performance is validated through experiments spanning various domains and data types, including the synthetic adding problem, sentiment classification with IMDB data, digit classification using MNIST, and LLMing with the Penn TreeBank dataset. These datasets present a spectrum of sequence lengths and complexity levels, offering a thorough testbed for the proposed model. Results reveal that MGU achieves comparable accuracy to GRU across these diverse tasks while achieving a notable reduction in training time and computational overhead.

• Adding Problem: MGU and GRU demonstrated nearly indistinguishable mean squared errors after extensive epochs, exhibiting the capability to handle sequence lengths of around 50 efficiently.
• IMDB Sentiment Analysis: MGU slightly outperformed GRU in accuracy while executing nearly three times faster per epoch, showcasing the model's appropriateness for moderate-length sequences.
• MNIST: For both row-based and pixel-based sequence interpretations, MGU matched or surpassed GRU in classification accuracy, evidencing its adaptability to sequence lengths up to 784. Notably, MGU trained significantly faster, making it advantageous for practical large-scale applications.
• Penn TreeBank: On this word-level prediction task, MGU had slightly higher perplexity than GRU but validated its parameter efficiency, suggesting potential benefits in scenarios constrained by computational resources.

Implications and Future Directions

The introduction of MGU presents implications for theoretical and practical advancements in the area of sequence modeling. Practically, its reduced parameter load makes it attractive for deployment in resource-limited environments. Theoretically, its simplified architecture facilitates deeper exploration into the inner workings and optimization of RNNs, potentially contributing to the development of more refined learning algorithms and evaluation methodologies.

Future investigations could involve extending training durations and applying regularization strategies to further enhance MGU's performance. Expanding the range of tasks and datasets explored could additionally reveal unexplored strengths or address potential limitations of the proposed unit.

Overall, this paper introduces a cogent alternative to existing RNN architectures, proposing a pathway for reducing complexity without compromising performance, thereby contributing valuable insights into the landscape of sequential neural processing.