Recurrent Neural Network Regularization (1409.2329v5)

Abstract: We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include LLMing, speech recognition, image caption generation, and machine translation.

• The paper presents a novel dropout method applied exclusively to non-recurrent LSTM connections to significantly reduce overfitting.
• Experiments show marked improvements, with reduced perplexity in language modeling and boosted BLEU scores and accuracy in speech recognition and translation.
• The approach preserves long-term dependencies while enhancing generalization across diverse tasks, highlighting its broad applicability in sequential data processing.

Recurrent Neural Network Regularization

Introduction

The paper addresses the challenge of overfitting in Recurrent Neural Networks (RNNs), specifically those incorporating Long Short-Term Memory (LSTM) units. While dropout is a well-regarded regularization method for neural networks, its application to RNNs and LSTMs is non-trivial and frequently unsuccessful. This work presents a novel method of applying dropout to LSTMs that mitigates overfitting effectively across a variety of tasks, including LLMing, speech recognition, image caption generation, and machine translation.

Regularization Approach

LSTMs enhance RNNs with the capability to maintain long-term dependencies through memory cells, which necessitates a specialized approach to regularization. The authors propose applying dropout exclusively to non-recurrent connections within LSTMs. This targeted application preserves the integrity of long-term information storage while infusing the model with the regularization benefits of dropout. The implementation involves applying the dropout operator only on non-recurrent connections, ensuring the state transitions crucial to the network's memory are not disrupted by noise.

Experiments and Results

The paper evaluates the efficacy of the proposed dropout technique on several tasks, demonstrating its utility across domains:

LLMing

On the Penn Tree Bank dataset, regularized LSTMs with two layers showed significant reductions in word-level perplexity. The large regularized LSTM achieved a test set perplexity of 78.4, outperforming non-regularized counterparts and previously reported benchmarks. Ensembles of regularized LSTMs yielded further improvements, with a test set perplexity of 68.7 when averaging 38 models.

Speech Recognition

The method was tested on an internal Google Icelandic Speech dataset, consisting of 93k utterances. Regularized LSTMs yielded higher validation frame accuracy (70.5%) compared to non-regularized models (68.9%), underscoring the approach's capacity to generalize better despite added training noise.

Machine Translation

For the WMT'14 English to French translation task, the regularized LSTM demonstrated a reduction in test perplexity from 5.8 to 5.0 and improved BLEU scores from 25.9 to 29.03. Although it did not surpass traditional phrase-based systems, the enhancements highlight the potential of this regularization approach in translation tasks.

Image Caption Generation

In the image caption generation context, incorporating dropout improved the test perplexity and BLEU scores (7.99 and 24.3, respectively) over non-regularized models. Notably, the performance of a regularized single model was comparable to an ensemble of non-regularized models, suggesting dropout's efficacy in producing robust single models.

Implications and Future Work

The presented dropout application method for LSTMs offers a significant stride in regularizing RNN architectures without compromising their ability to manage long-term dependencies. The empirical results across diverse domains suggest a broad applicability and highlight the potential for better-performing, generalized models in various tasks involving sequential data.

Future research could explore extending this regularization technique to other variants of RNNs, assessing its impact on even larger datasets, and refining the dropout application methods to enhance task-specific performance further. The observed improvements in machine translation and caption generation suggest that more sophisticated sequence modeling tasks could benefit significantly from further optimization and exploration based on this approach.

In conclusion, the paper demonstrates a straightforward yet effective method of applying dropout in LSTMs, which significantly curtails overfitting and universally enhances model performance across a spectrum of applications. This advancement in regularization techniques contributes meaningfully to the optimization of RNNs, particularly LSTMs, anchoring their reliability and efficiency in practical, real-world scenarios.