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.

翻译：本文对自然语言处理（NLP）领域中的混合与集成深度学习模型进行了全面探讨，揭示了其在情感分析、命名实体识别、机器翻译、问答系统、文本分类、文本生成、语音识别、文本摘要及语言建模等多种任务中的变革性潜力。本文系统介绍了各项任务，阐述了从循环神经网络（RNNs）到基于Transformer的模型（如BERT）等关键架构，并评估了其性能、挑战与计算需求。文章重点强调了集成技术的适应性，凸显了其在提升各类NLP应用方面的能力。同时，针对实现过程中的挑战——包括计算开销、过拟合以及模型可解释性复杂性——进行了探讨，并分析了可解释性与性能之间的权衡。本综述作为一份简明而宝贵的指南，综合了对任务、架构及挑战的见解，为希望通过集成深度学习推动NLP语言驱动应用的研究者与实践者提供了整体性视角。