基于BERT的文本生成算法 -计算机科学与技术专业

摘要

随着自然语言处理技术的不断发展，文本生成作为其中的关键任务受到广泛关注。基于BERT的文本生成算法旨在利用预训练语言模型的强大语义表示能力，解决传统文本生成方法在语义连贯性和表达多样性方面的不足。实验结果表明，该算法在多个基准数据集上取得了显著优于现有方法的表现，特别是在长文本生成、语义一致性以及领域迁移能力方面展现出明显优势。与传统生成模型相比，本研究的最大创新点在于充分利用了BERT的双向上下文理解能力，并通过针对性优化有效解决了预训练模型应用于文本生成时可能遇到的过拟合问题，为后续研究提供了新的思路和技术参考。此外，该算法还具备良好的可扩展性，能够方便地集成到不同的应用场景中，为实现高质量、高效率的自动化文本生成提供了可靠保障。

关键词：文本生成，BERT，自适应掩码机制，多阶段微调

ABSTRACT

With the continuous development of natural language processing (NLP), text generation as one of the key tasks has attracted extensive attention. The BERt-based text generation algorithm aims to take advantage of the powerful semantic representation ability of pre-trained language models, and solve the shortcomings of traditional text generation methods in semantic coherence and ex pression diversity. The experimental results show that the proposed algorithm performs significantly better than the existing methods on multiple benchmark datasets, especially in the aspects of long text generation, semantic consistency and domain migration. Compared with the traditional generation model, the biggest innovation of this study is that it makes full use of BERT's bidirectional context understanding ability, and effectively solves the overfitting problem that may be encountered when the pre-trained model is applied to text generation through targeted optimization, providing new ideas and technical references for subsequent research. In addition, the algorithm also has good scalability and can be easily integrated into different application scenarios, which provides a reliable guarantee for realizing high quality and high efficiency automatic text generation.

Keywords: text generation, BERT, Adaptive mask mechanism, Multistage fine tuning

摘要 I

ABSTRACT II

第一章绪论 1

1.1 研究背景及意义 1

1.2 国内外研究现状 1

1.3 本文研究方法 2

第二章 BERT文本生成算法原理 3

2.1 BERT模型架构分析 3

2.2 文本生成机制探讨 3

2.3 关键技术解析 4

第三章基于BERT的文本生成优化策略 6