Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling (1412.3555v1)

Abstract: In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.

• The paper demonstrates that gating mechanisms significantly improve sequence modeling, outperforming traditional tanh units in music and speech tasks.
• Researchers ensured fair comparisons by equating parameter counts and using RMSProp with gradient clipping to optimize training.
• Results indicate that while both GRU and LSTM offer advantages over simple RNNs, their performance is context-dependent, suggesting areas for future focused studies.

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

The paper "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling" by Chung et al. presents a thorough empirical analysis comparing various types of recurrent units in Recurrent Neural Networks (RNNs). Specifically, the paper focuses on evaluating traditional hyperbolic tangent ($\tanh$) units, Long Short-Term Memory (LSTM) units, and Gated Recurrent Units (GRU) on sequence modeling tasks, which includes polyphonic music modeling and speech signal modeling.

Introduction

Recent years have seen significant advancements in the utilization of Recurrent Neural Networks (RNNs) for various sequential tasks such as machine translation and speech recognition. One notable observation is that these advancements are often driven by RNNs with sophisticated recurrent hidden units, rather than the vanilla RNNs utilizing simple activation functions like $\tanh$.

The paper's primary objective is to compare the efficacy of LSTM units and the more recently proposed GRU in relation to the conventional $\tanh$ units. LSTM has been well-documented to handle long-term dependencies effectively. GRU, introduced more recently, aims to achieve similar performance with a potentially simpler architecture.

Background on Recurrent Neural Networks

RNNs extend conventional feedforward neural networks by incorporating a recurrent hidden state, allowing for variable-length sequence input. Traditional RNNs, which implement updates via smooth, bounded functions such as the hyperbolic tangent function, often encounter difficulties with vanishing and exploding gradients, particularly when capturing long-term dependencies.

Two solutions have emerged to address these issues:

1. Enhanced learning algorithms such as clipped gradients and second-order methods.
2. Sophisticated activation functions incorporating gating mechanisms, exemplified by LSTM and GRU.

Gated Recurrent Neural Networks

Long Short-Term Memory (LSTM) Units

LSTM units maintain a memory cell that can be manipulated via input, forget, and output gates. These gates regulate the addition of new content, forgetting of previous content, and exposure of cell state, respectively, thus making it easier to capture long-range dependencies.

Gated Recurrent Units (GRU)

GRU simplifies memory management by combining the forget and input gates into a single update gate and merging the cell state with the hidden state. This design aims to streamline the model's complexity while retaining its capability to manage dependencies of varying lengths.

Experiments

Tasks and Datasets

The paper evaluates the recurrent units on two tasks:

1. Polyphonic music modeling using datasets such as Nottingham, JSB Chorales, MuseData, and Piano-midi.
2. Speech signal modeling on two internal datasets from Ubisoft: short sequences (Ubisoft A) and long sequences (Ubisoft B).

Models and Settings

For both tasks, RNNs with LSTM units, GRU, and $\tanh$ units were trained, ensuring approximately equivalent parameter counts across models to maintain a fair comparison. The models were trained using RMSProp with weight noise and gradient clipping to mitigate exploding gradients. Hyperparameters, including learning rates, were selected based on validation performance.

Results and Analysis

The results reveal:

• Polyphonic Music Modeling: GRU-RNN outperformed other models on most datasets, although the performance gap was not substantial.
• Speech Signal Modeling: Both gated units (LSTM and GRU) significantly outperformed the $\tanh$-RNN, with each showing superior performance on one of the Ubisoft datasets.

In terms of convergence speed and generalization, models with gating mechanisms demonstrated clear advantages over traditional units, as evidenced by their learning curves.

Conclusion

The empirical evaluation confirms that gating mechanisms in recurrent units significantly enhance performance on sequence modeling tasks, especially in more complex scenarios like raw speech signal modeling. However, the results do not definitively favor one gating mechanism over the other; the choice between LSTM and GRU may be context-dependent.

Future research should further dissect the contributions of individual components within these units to better understand their respective impacts on learning efficiency and capacity. Detailed, task-specific studies would also facilitate a more nuanced understanding of when to favor one type of unit over another.