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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio
TL;DR
The paper addresses uncertainty about how LSTM and the newer GRU compare beyond machine translation by empirically evaluating them against tanh units. Across polyphonic music and raw speech sequence modeling, gated units outperform tanh units, while no definitive winner emerges between LSTM and GRU.
Problem
The paper investigates whether GRU performance comparable to LSTM in machine translation also applies to other sequence-modeling tasks.
Method
The authors compare LSTM, GRU, and tanh recurrent units on polyphonic music modeling and raw speech signal modeling, using approximately equal parameter counts.
Results
Gated units clearly outperform tanh units, especially on raw speech signal modeling, while the experiments do not establish whether LSTM or GRU is better.
Takeaways & Limitations
Both LSTM and GRU are supported as stronger alternatives to traditional tanh units for the evaluated sequence-modeling tasks.
Takeaways & Limitations
The experiments are preliminary and do not isolate the contributions of individual gating components, requiring more thorough future experiments.
Abstract
from arXiv · showhide
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.
1 Introduction
The paper evaluates LSTM and GRU gated recurrent units against traditional tanh units for sequence modeling across music and speech data. It finds gated units generally stronger, with GRU sometimes outperforming LSTM under matched parameter budgets.
- Motivation: Gated recurrent units are evaluated because sophisticated recurrent units appeared in successful applications more often than vanilla RNNs.
- Motivation: LSTM is established for sequence tasks with long-term dependencies, whereas GRU had more limited prior use in machine translation.
- Evaluation scope: The study compares LSTM, GRU, and tanh recurrent units on sequence-modeling tasks.The evaluation covers three polyphonic music datasets and two internal raw-speech datasets.
- Headline result: With a fixed number of parameters on some datasets, GRU can outperform LSTM in convergence time, parameter updates, and generalization.
2 Background: Recurrent Neural Network
RNNs process variable-length sequences through recurrent hidden states, but standard training can struggle with long-term dependencies because gradients vanish or explode. Gated units address this challenge through more sophisticated recurrent mechanisms.
- RNN formulation: An RNN handles variable-length input by making each hidden-state activation depend on the previous hidden state.
- RNN formulation: Given a sequence x, the RNN updates its recurrent hidden state h_t using the previous state and the current input.
- Traditional units: The hidden-state update traditionally applies a smooth, bounded nonlinear function such as a sigmoid or hyperbolic tangent.
- Generative modeling: Generative RNNs model a probability distribution over variable-length sequences by predicting each next element and using a special end-of-sequence symbol.
- Motivation for gating: Long-term dependency learning is difficult because gradients tend to vanish or explode, motivating gated recurrent units such as LSTM and GRU.
3 Gated Recurrent Neural Networks
LSTM and GRU units use gates and additive state updates to preserve information across time, while differing in memory exposure and information-flow control. The paper motivates and evaluates their comparison beyond machine translation, but treats the experiments as preliminary.
- Long Short-Term Memory: LSTM units maintain memory and use gates to control forgetting, input addition, and exposure of stored content.The output gate controls how much memory content is exposed to other units.
- Gated Recurrent Unit: GRUs adaptively capture dependencies at different time scales through update and reset gates without a separate memory cell.The update gate interpolates between the previous activation and a newly computed candidate, while the reset gate can suppress the previous state.
- Shared properties: Both LSTM and GRU use additive updates that preserve existing content and add new content instead of replacing the activation at every step.This differs from traditional recurrent units, which overwrite their activation with a new value.
- Shared properties: Additive updates help units retain features over long sequences and create shortcut paths that ease error back-propagation through time.The shortcut effect can reduce difficulty from vanishing gradients when gating units are nearly saturated at 1.
- Key differences: LSTM controls memory exposure with an output gate, whereas GRU exposes its full state and places reset control in candidate-state computation.The units also differ in whether they independently control the amount of newly computed content added to memory or activation.
- Motivation and scope: The paper compares these units because their relative performance beyond machine translation was unclear, while noting that the experiments are preliminary.The authors state that more thorough experiments are needed to separate the contributions of individual gating components; their preliminary tests found the two GRU reset formulations performed equally well.
4 Experiments Setting
The experiments compare LSTM, GRU, and tanh recurrent units for sequence modeling on polyphonic music and raw speech datasets using approximately equal-sized models.
- Tasks and datasets: The study evaluates LSTM-RNN, GRU-RNN, and tanh-RNN models on polyphonic music and speech signal modeling.The music data comprise multiple polyphonic datasets, while the speech data are internal raw-audio datasets.
- Polyphonic music: The music datasets represent each sequence symbol as a binary vector with 93 to 108 dimensions.The datasets are Nottingham, JSB Chorales, MuseData, and Piano-midi.
- Speech signal modeling: The speech datasets use 20 consecutive raw-audio samples to predict the following 10 samples.Ubisoft A contains sequences of length 500, whereas Ubisoft B contains sequences of length 8,000.
- Model comparison: Each task trains models with approximately the same number of parameters to enable a fair comparison among recurrent units.The models were intentionally kept small to avoid overfitting distracting the comparison.
- Optimization: Training uses RMSProp, weight noise, gradient-norm rescaling, validation-based learning-rate selection, and early stopping.The learning rate is selected from 10 randomly chosen log-uniform candidates, and gradients larger than 1 are rescaled.
5 Results and Analysis
Gated recurrent units generally outperform traditional tanh units, while GRU and LSTM results vary across datasets and remain inconclusive in direct comparison.
- Polyphonic music: On polyphonic music datasets, GRU-RNN outperformed LSTM-RNN and tanh-RNN except on Nottingham, where the models performed closely.The comparison covers the music datasets summarized in Table 2.
- Speech signal modeling: On both Ubisoft speech datasets, GRU-RNN and LSTM-RNN clearly outperformed tanh-RNN.LSTM-RNN was best on Ubisoft A, while GRU-RNN was best on Ubisoft B.
- Convergence: GRU-RNN made faster progress than the other units on music datasets in both parameter updates and actual CPU time.Figure 2 shows learning curves against iteration count and wall-clock time, with negative-log likelihood on the y-axis.
- Convergence: Although tanh-RNN required less computation per update on Ubisoft datasets, it made little progress per update and stopped at a worse level.The result concerns the learning curves shown in Figure 3.
- Overall comparison: The results indicate advantages for gating units, but do not conclusively establish whether LSTM or GRU is better overall.The preferred gated unit may depend on the dataset and corresponding task.
6 Conclusion
The paper evaluates tanh, LSTM, and GRU recurrent units on sequence modeling across polyphonic music and raw speech data. Gated units outperform tanh units, while the experiments remain preliminary and do not establish whether LSTM or GRU is better.
- The evaluation compares traditional tanh, LSTM, and GRU recurrent units on polyphonic music and raw speech sequence-modeling datasets.
- Both gated units outperform the traditional tanh unit, with the difference more evident for raw speech signal modeling.
- The learning curves report training and validation negative-log likelihood against epochs and wall-clock time for different recurrent units.
- The experiments are preliminary, requiring more thorough studies to isolate how individual gating components contribute to learning.