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.

该研究综述了自然语言处理中混合和集成深度学习模型的广泛探索，揭示了它们在情感分析、实体识别、机器翻译、问答、文本分类、生成、语音识别、摘要生成和语言建模等多种任务中具有的变革潜力。研究系统地介绍了每个任务，从循环神经网络 (RNNs) 到基于 Transformer 的模型如 BERT，阐述了它们的性能、挑战和计算需求。强调了集成技术的适应性，突出了它们增强各种自然语言处理应用的能力。还讨论了在实施过程中的挑战，包括计算负载、过拟合和模型解释复杂性，以及解释性和性能之间的权衡。作为精炼而宝贵的指南，该综述综合了有关任务、模型结构和挑战的见解，为研究人员和从事自然语言处理的从业者提供了一个综合的视角，以通过集成深度学习在自然语言处理中推进基于语言的应用。