摘 要
随着深度学习技术的快速发展，自然语言处理（NLP）领域取得了显著进展，但模型性能优化仍面临诸多挑战。本研究旨在探索适用于复杂自然语言任务的深度学习模型优化方法，以提升模型效率与效果。研究背景基于当前深度学习模型在计算资源消耗、训练时间及泛化能力等方面的局限性，提出通过改进优化算法、调整网络结构以及引入迁移学习等手段来解决这些问题。具体而言，本文首先分析了主流优化算法如Adam、SGD等在自然语言处理中的适用性，并提出一种自适应优化策略，结合动量项与学习率动态调整机制，有效缓解梯度消失和收敛速度慢的问题；其次，针对模型参数冗余问题，采用知识蒸馏技术压缩模型规模，同时保持较高的预测精度；此外，通过预训练与微调相结合的方式，增强模型对小样本数据集的适应能力。
关键词：深度学习优化 自然语言处理 自适应优化策略
Abstract
With the rapid development of deep learning technology, significant progress has been made in the field of natural language processing (NLP), but model performance optimization still faces many challenges. This study aims to explore deep learning model optimization methods suitable for complex natural language tasks to improve model efficiency and effectiveness. Based on the limitations of the current deep learning model in terms of computing resource consumption, training time and generalization ability, this paper proposes to solve these problems by improving the optimization algorithm, adjusting the network structure and introducing transfer learning. Specifically, this paper first analyzes the applicability of mainstream optimization algorithms such as Adam and SGD in natural language processing, and proposes an adaptive optimization strategy combining the dynamic adjustment mechanism of momentum term and learning rate to effectively alleviate the problems of gradient disappearance and slow convergence; secondly, for the redundancy of model parameters, with high prediction accuracy. In addition, enhance the adaptability of the model to small sample data sets through the combination of pre-training and fine-tuning.
Keyword:Deep Learning Optimization Natural Language Processing Adaptive Optimization Strategy
目 录
引言 1
1深度学习模型基础与优化需求 1
1.1自然语言处理任务概述 1
1.2深度学习模型的基本架构 2
1.3当前模型优化的主要挑战 2
1.4优化方法的研究意义 2
2参数优化策略在NLP中的应用 3
2.1参数初始化技术研究 3
2.2学习率调整机制分析 3
2.3正则化方法的改进探索 4
2.4稀疏性优化对性能的影响 4
3结构优化提升模型效率 5
3.1轻量化网络设计方法 5
3.2模型剪枝技术的应用 5
3.3知识蒸馏在NLP中的实践 6
3.4动态计算图的优化策略 6
4数据与算法协同优化方法 6
4.1数据增强对模型性能的作用 6
4.2对抗训练的优化效果评估 7
4.3多任务学习的优化策略研究 7
4.4自监督学习方法的改进方向 8
结论 8
参考文献 10
致谢 11
