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

TL;DR

This review comprehensively examines hybrid and ensemble deep learning models in NLP, discussing architectures, applications, challenges, and their potential to improve various language processing tasks.

Contribution

It provides a systematic overview of architectures, applications, and challenges of hybrid and ensemble deep learning models in NLP, serving as a guide for researchers and practitioners.

Findings

Ensemble techniques enhance NLP task performance.

Transformer-based models like BERT are central to modern NLP.

Challenges include computational overhead and overfitting.

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.