摘 要
自然语言生成（NLG）作为人工智能领域的重要分支，近年来随着深度学习技术的发展取得了显著进展。本文聚焦于基于深度学习的自然语言生成技术研究，旨在探索更高效、精准的语言生成模型。通过对现有深度学习框架如循环神经网络（RNN）、长短时记忆网络（LSTM）、门控循环单元（GRU）以及Transformer架构的深入分析，结合大规模语料库训练，提出了一种融合多模态信息的新型生成模型。该模型不仅能够处理文本到文本的任务，还创新性地实现了跨模态内容生成，如从图像到描述文本的转换。实验结果表明，在多个基准数据集上，所提出的模型在BLEU、ROUGE等评价指标上均优于传统方法，特别是在长文本生成和语义连贯性方面表现突出。此外，针对现有模型存在的可控性不足问题，引入了条件约束机制，使得生成过程更加灵活可控。本研究的主要贡献在于突破了单一模态限制，拓展了自然语言生成的应用场景，并为后续研究提供了新的思路和技术参考，对推动智能写作、自动摘要、对话系统等领域的发展具有重要意义。
关键词：自然语言生成 深度学习 多模态信息融合
Abstract
Natural Language Generation (NLG), as a critical branch of artificial intelligence, has witnessed significant advancements in recent years with the development of deep learning technologies. This paper focuses on the research of deep learning-based NLG techniques, aiming to explore more efficient and accurate language generation models. By conducting an in-depth analysis of existing deep learning fr ameworks such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and the Transformer architecture, combined with large-scale corpus training, a novel generative model that integrates multimodal information is proposed. This model not only handles text-to-text tasks but also innovatively achieves cross-modal content generation, such as converting images to desc riptive texts. Experimental results demonstrate that on multiple benchmark datasets, the proposed model outperforms traditional methods in evaluation metrics such as BLEU and ROUGE, particularly excelling in long text generation and semantic coherence. Additionally, addressing the issue of insufficient controllability in existing models, a conditional constraint mechanism is introduced, making the generation process more flexible and controllable. The primary contribution of this study lies in breaking the limitations of single modality, expanding the application scenarios of natural language generation, and providing new ideas and technical references for future research, which is of great significance for advancing intelligent writing, automatic summarization, dialogue systems, and other related fields.
Keyword:Natural Language Generation Deep Learning Multimodal Information Fusion
目 录
引言 1
1自然语言生成技术概述 1
1.1深度学习基础理论 1
1.2自然语言生成发展历程 2
1.3关键技术与方法综述 2
2深度学习模型架构研究 3
2.1序列到序列模型分析 3
2.2变分自编码器应用 3
2.3注意力机制优化探索 4
3生成质量评估体系构建 4
3.1主观评价指标设计 4
3.2客观评价标准建立 5
3.3多维度综合评估方法 5
4技术应用及未来展望 6
4.1对话系统中的应用 6
4.2文本创作领域实践 7
4.3发展趋势与挑战应对 7
结论 7
参考文献 9
致谢 9
