用变压器进行自然语言处理:一篇综述
Georgiana Tucudean1, Marian Bucos1, Bogdan Dragulescu1
1Communications Department, Politehnica University Timișoara, Timișoara, Timiș, România.
PeerJ. Computer science
|August 15, 2024
概括
本研究审查了自然语言处理 (NLP) 任务的深度学习架构,重点关注 BERT 和 GPT 等变压器模型. 它总结了当前的NLP应用程序,模型和数据集,强调了领域的挑战.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 自然语言处理 (NLP) 任务越来越多地使用各种深度学习架构来解决.
- 基于变压器的模型已经成为各种NLP应用的高效解决方案.
研究的目的:
- 总结NLP任务的用例和主要架构.
- 介绍基于变压器的解决方案,特别是来自变压器的双向编码器表示 (BERT) 和生成预训练 (GPT) 架构.
- 提供关于NLP领域当前状态的见解,包括应用程序,语言模型和数据集类型.
主要方法:
- 采用了系统审查策略,包括识别最近的变压器研究.
- 应用过器来选择一致的研究,并定义了包含/排除标准.
- 从选定的文章中评估和讨论了方法和架构.
主要成果:
- 该审查系统地总结和比较分析了利用变压器架构的NLP应用程序.
- 关键变压器模型,如BERT和GPT,因其在NLP任务中的效率而受到重视.
- 对当前的NLP应用程序,语言模型和数据集类型的洞察力被产生.
结论:
- 变压器架构在处理自然语言处理任务方面取得了重大进展.
- 该研究提供了对当前NLP趋势和变压器范式中的挑战的基本理解.
- 进一步的研究可以建立在这些发现的基础上,探索新的NLP应用和模型改进.
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