Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

An Analysis of Gated Recurrent Neural Networks in Sequence Modeling

The paper compares different recurrent units in RNNs - LSTMs and GRUs - with the hypothesis that specialized units outperform vanilla RNNs.

Historical Background:

Decades ago, Recurrent Neural Networks (RNNs) were proposed for handling variable-length sequential input through a shared hidden state. However, practical limitations arose due to vanishing and exploding gradient problems during training. The introduction of the long-short term memory (LSTM) recurrent unit addressed these challenges by confidently capturing long-term dependencies.

Subsequently, the Gated Recurrent Unit (GRU) emerged with a similar goal of tracking long-term dependencies while addressing gradient problems. LSTMs employ input, forget, and output gates, while GRUs use a reset gate and an update gate. Both retain memory from prior activations, allowing preservation of features for extended periods and reducing the risk of the vanishing gradient problem.

Recurrent Neural Network (RNN):

Designed for sequence modeling, RNNs capture information from past inputs for current predictions. However, the vanishing gradient problem limits their ability to capture long-term dependencies. A technology that emerged in the early 1980s, it is a model that was studied to help machines learn, understand, and produce human language, a goal of early artificial intelligence research. This required the processing of sequence data and was studied in order to respond to new input while remembering previous information. RNNs were the cornerstone of the current development of LSTMs, providing scalability to a wide range of applications. It is a model that has played a key role in the emergence of new models such as Transformer.

Long Short-Term Memory (LSTM):

Specifically addressing the vanishing gradient problem, LSTMs have a complex structure enabling selective retention or forgetting of information in long sequences, making them effective for capturing long-term dependencies. A form of model that evolved from RNNs and is similarly used to process sequence data. It is structurally similar to an RNN, but overcomes the limitations of RNNs, which lack the ability to store and retain information over long periods of time through a much more complex computational process. In a structured LSTM cell, weights are passed through an input gate, forget gate, and output gate to determine whether to retain information over a long period of time.

Gated Recurrent Unit (GRU):

Similar to LSTM but with a simpler structure, GRUs address the vanishing gradient problem. They use update and reset gates to control information flow, deciding how much past information to forget and how much new information to incorporate.

**image source: https://wikidocs.net/166316

Literature Review:

In this experiment, the authors aimed to compare the performance of different types of recurrent neural network (RNN) units, specifically Long Short-Term Memory (LSTM) units, Gated Recurrent Units (GRUs), and traditional tanh units. The primary objective was to conduct a fair comparison by ensuring that each model had approximately the same number of parameters. The models were intentionally kept small to avoid overfitting, which could potentially impact the comparison.

Model Configuration:

Sizes of Models: The authors trained three models for each task, each using LSTM, GRU, or tanh units. The sizes of these models were chosen to have roughly the same number of parameters.

Training Techniques: The models were trained using RMSProp, and weight noise with a fixed standard deviation of 0.075 was applied. The norm of the gradient was rescaled to 1 at every update to prevent exploding gradients.

Learning Rate Selection: The learning rate was selected to maximize validation performance out of 10 randomly chosen log-uniform candidates.

Results:

The results were presented in terms of negative log-probabilities on different music datasets (Nottingham, JSB Chorales, MuseData, Piano-midi) and Ubisoft datasets (Ubisoft A and Ubisoft B). The performance of each model was evaluated on both training and test sets.

Music Datasets:

The GRU-RNN outperformed LSTM-RNN and tanh-RNN on most music datasets, except for Nottingham where the performance was comparable among the three models.

Ubisoft Datasets:

Both GRU-RNN and LSTM-RNN outperformed tanh-RNN on the Ubisoft datasets. LSTM-RNN performed better on Ubisoft A, while GRU-RNN performed better on Ubisoft B.

Observations:

Advantages of Gating Units: The results indicated that RNNs with gating units (GRU-RNN and LSTM-RNN) generally outperformed the more traditional tanh-RNN. Gating units led to faster convergence and better final solutions.

Comparison of LSTM and GRU: The study did not provide conclusive evidence regarding the superiority of either LSTM or GRU, suggesting that the choice of the gated recurrent unit may heavily depend on the specific dataset and task.

Learning Curves: Visualizing the learning curves revealed that GRU-RNN made faster progress in terms of the number of updates and actual CPU time, particularly evident in the music datasets. Tanh-RNN, while computationally more efficient per update, struggled to make significant progress in the Ubisoft datasets.

Biography:

1. Junyoung Chung

Staff Research Scientist at DeepMind
PhD in Machine Learning at Montreal University
Contributed to generate AlphaCode, AlphaStar
AlphaCode and AlphaStar are both cognitive programming models, consisting of OpenAI

2. Caglar Gulcehre

Former Staff Research Scientist at DeepMind
MILA Supervision PhD
Contributed to constructing algorithms in OpenAI
Author of “Regularized Behavior Value Estimation” paper

3. Kyunghyun Cho

Professor of computer and data science at NYU
Contributed to developing the Sequence to Sequence learning model with the Google Brain team in 2014
Regarded as an important figure in the research and development of modern artificial intelligence

4. Yoshua Bengio

One of the pioneers in the field of Deep Learning
Full Professor of Computer Science and operating a research team at the University of Montreal
Founder of the Montreal Institute of Artificial Intelligence (MILA) and significantly contributed to the A.I field
In 2018, Awarded the Turing Prize

Social Impact

Positive Impact

1. Cost-Effective Natural Language Processing:

The reduced model computing and learning time associated with GRUs enable companies to develop cost-effective natural language processing (NLP) models. This reduction in costs makes advanced language processing technology more accessible, allowing a broader range of companies to implement and deploy language models without prohibitive expenses.

2. Increased Choices and Convenience:

The cost reduction facilitated by these recurrent neural networks enables companies to offer substitutes for expensive models. This increase in affordability leads to a greater variety of choices and enhanced convenience for users. Users can access a wider range of applications and services based on advanced language processing models.

Negative Impact

1. Reliability for Specialized Tasks:

LSTMs are known to perform well with complex data and long sequences. However, if the output of LSTMs is less accurate, it can be unreliable for users relying on models for specialized tasks. For applications where precision and reliability are crucial, such as medical diagnoses or critical decision-making, inaccuracies in LSTM outputs can have significant consequences.

Industry Practitioner

Efficiency Advantage

Fewer parameters due to a simple network structure means inducing low costs. Faster training time and lower memory requirements make it possible to produce alternative models for companies that cannot build large models.

Flexibility

The GRU can be applied to sequence data of various types, offering similar performance to LSTM, but with cost-saving benefits.

Implementation and findings

We evaluated the performance of GRU and LSTM models on the NLTK reuters dataset. The NLTK Reuters dataset is a well-known collection of news documents widely used for text classification tasks.

Training curves for GRU

Training curves for LSTM

Results

When examining the learning curves, which illustrate the model's performance over training iterations or epochs, it was observed that both the GRU and LSTM models exhibited similar trends. The similarity in learning curves suggests that both GRU and LSTM models demonstrated comparable abilities to learn from the training data, and both models are adapting similarly to the dataset. Compared to the train loss and accuracy of the LSTM and GRU models by epoch, there is no significant difference, and the time graph shows also have similar result. Therefore, this represents that the GRU model performs similarly to the LSTM on this dataset. However, it is necessary to use a larger dataset to consider and determine the time efficiency.

Peer review:

Aishwarya

Originality: 6/10

The paper offers a valuable empirical comparison of LSTM, GRU, and tanh units in sequence modeling tasks, particularly in polyphonic music and raw speech signal datasets. While not entirely groundbreaking, the study provides a novel contribution by evaluating and comparing these units across diverse datasets. Related work is appropriately referenced.

Quality: 8/10

The paper exhibits technical soundness through well-designed experiments, employing a fixed parameter approach. The methodology is comprehensive, covering polyphonic music and speech signal modeling with appropriate metrics. The authors transparently acknowledge study limitations, notably the lack of a conclusive LSTM vs. GRU comparison.

Clarity: 7/10

While generally well-written, there's room for enhanced clarity, especially in task and dataset explanations. Minor improvements in transitions, particularly in the conclusion, would enhance readability.

Significance: 7/10

The paper's significance lies in practical implications for choosing recurrent units. The demonstrated advantages of GRU over traditional tanh units provide valuable insights for researchers and practitioners. Inclusion of diverse datasets enhances relevance.

Overall : 7/10

Hwijong Im

Originality: 7/10

This paper provides a valuable empirical comparison between LSTM and GRU models, comparing their efficiency in terms of computational complexity and learning time. Although the superiority of LSTMs is already widely recognized, this study aims to contribute to the literature by demonstrating the efficiency and cost-effectiveness of GRU models.

Quality: 6/10

The study pointed out to compare the performance of LSTM and GRU models using multiple datasets to demonstrate that the GRU model performs similarly to the LSTM model, however, due to time limitations, many datasets were not utilized.

Clarity: 8/10

The purpose of this study is to evaluate the performance of LSTM and GRU models and determine whether the GRU model, which has a cost advantage, performs similarly to LSTM. With more references and datasets for comparison, the objective of the study would be more clear.

Significance: 8/10

This paper is as not much as important if company have enough resources to utilize LSTM models, as it simply demonstrates the cost-effectiveness of using an alternative model to LSTM.

Overall : 8/10

References

[1] Léon Bottou and Patrick Gallinari. "A framework for the cooperation of learning algorithms." Advances in neural information processing systems 3 (1990).

[2] Pre-Trained LSTM model for Sentiment Analysis (Accessed 2023).

[3] Understanding GRU networks (Accessed 2023).

[4] Understanding Gated Recurrent Unit (GRU) in Deep Learning (Accessed 2023).

[5] Cho, K., et al. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078 (2014).

[6] Chung, J., et al. "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3555 (2014).

[7] Olah, C. "Understanding LSTM Networks." colah's blog (2015).

Team Members

Hwijong Im

Aishwarya Nagendra Kumar