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

This review presents a comprehensive exploration of hybrid and ensemble deep learning models within <PRE_TAG>Natural Language Processing (NLP)</POST_TAG>, shedding light on their transformative potential across diverse tasks such as <PRE_TAG>Sentiment Analysis</POST_TAG>, Named Entity Recognition, <PRE_TAG>Machine Translation</POST_TAG>, <PRE_TAG>Question Answering</POST_TAG>, Text Classification, Generation, <PRE_TAG>Speech Recognition</POST_TAG>, <PRE_TAG>Summarization</POST_TAG>, and Language Modeling. The paper systematically introduces each task, delineates key architectures from <PRE_TAG>Recurrent Neural Networks (RNNs)</POST_TAG> to <PRE_TAG>Transformer-based models</POST_TAG> like <PRE_TAG>BERT</POST_TAG>, and evaluates their <PRE_TAG>performance</POST_TAG>, challenges, and computational demands. The adaptability of ensemble techniques is emphasized, highlighting their capacity to enhance various NLP applications. Challenges in implementation, including <PRE_TAG>computational overhead</POST_TAG>, <PRE_TAG>overfitting</POST_TAG>, and model interpretation complexities, are addressed alongside the trade-off between <PRE_TAG>interpretability</POST_TAG> and <PRE_TAG>performance</POST_TAG>. 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.