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A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing

Jianguo Jia, Wen Liang, Youzhi Liang

TL;DR

This paper surveys hybrid and ensemble deep learning approaches in NLP, tracing foundations from RNNs and CNNs to BERT and large language models. It analyzes how ensemble and hybrid techniques are applied across machine translation, question answering, and named entity recognition, detailing methods such as bagging, boosting, stacking, as well as LLM routing and speculative decoding. The discussion highlights the benefits of combining diverse architectures to improve robustness and generalization, while candidly addressing challenges in computation, interpretability, and maintenance. The work provides a practical overview for researchers and practitioners aiming to leverage ensemble and hybrid methods to advance language understanding and processing in real-world settings.

Abstract

This review presents a comprehensive exploration of hybrid and ensemble deep learning models within Natural Language Processing (NLP), shedding light on their transformative potential across diverse tasks such as Sentiment Analysis, Named Entity Recognition, Machine Translation, Question Answering, Text Classification, Generation, Speech Recognition, Summarization, and Language Modeling. The paper systematically introduces each task, delineates key architectures from Recurrent Neural Networks (RNNs) to Transformer-based models like BERT, and evaluates their performance, challenges, and computational demands. The adaptability of ensemble techniques is emphasized, highlighting their capacity to enhance various NLP applications. Challenges in implementation, including computational overhead, overfitting, and model interpretation complexities, are addressed alongside the trade-off between interpretability and performance. Serving as a concise yet invaluable guide, this review synthesizes insights into tasks, architectures, and challenges, offering a holistic perspective for researchers and practitioners aiming to advance language-driven applications through ensemble deep learning in NLP.

A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing

TL;DR

This paper surveys hybrid and ensemble deep learning approaches in NLP, tracing foundations from RNNs and CNNs to BERT and large language models. It analyzes how ensemble and hybrid techniques are applied across machine translation, question answering, and named entity recognition, detailing methods such as bagging, boosting, stacking, as well as LLM routing and speculative decoding. The discussion highlights the benefits of combining diverse architectures to improve robustness and generalization, while candidly addressing challenges in computation, interpretability, and maintenance. The work provides a practical overview for researchers and practitioners aiming to leverage ensemble and hybrid methods to advance language understanding and processing in real-world settings.

Abstract

This review presents a comprehensive exploration of hybrid and ensemble deep learning models within Natural Language Processing (NLP), shedding light on their transformative potential across diverse tasks such as Sentiment Analysis, Named Entity Recognition, Machine Translation, Question Answering, Text Classification, Generation, Speech Recognition, Summarization, and Language Modeling. The paper systematically introduces each task, delineates key architectures from Recurrent Neural Networks (RNNs) to Transformer-based models like BERT, and evaluates their performance, challenges, and computational demands. The adaptability of ensemble techniques is emphasized, highlighting their capacity to enhance various NLP applications. Challenges in implementation, including computational overhead, overfitting, and model interpretation complexities, are addressed alongside the trade-off between interpretability and performance. Serving as a concise yet invaluable guide, this review synthesizes insights into tasks, architectures, and challenges, offering a holistic perspective for researchers and practitioners aiming to advance language-driven applications through ensemble deep learning in NLP.
Paper Structure (13 sections)

This paper contains 13 sections.

Table of Contents

  1. Introduction
  2. Base Models
  3. Recurrent Neural Networks (RNN) in Sentiment Analysis
  4. Convolutional Neural Networks (CNN) in Sentiment Analysis
  5. Long Short-Term Memory Networks (LSTM)
  6. Bidirectional Encoder Representations from Transformers (BERT)
  7. Large Language Models
  8. Hybrid and Ensemble Models for NLP
  9. Machine Translation (MT)
  10. Question Answering Systems
  11. Named Entity Recognition (NER)
  12. Challenges in Implementing Ensemble Deep Learning for NLP
  13. Conclusion