使用转换器进行自然语言处理:综述

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Georgiana Tucudean, Marian Bucos, Bogdan Dragulescu, Catalin Daniel Caleanu
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引用次数: 0

摘要

自然语言处理(NLP)任务可以用多种深度学习架构来解决,而且许多不同的方法已被证明是高效的。本研究旨在简要总结 NLP 任务的用例和主要架构。本研究针对 NLP 任务提出了基于变换器的解决方案,如来自变换器的双向编码器表示(BERT)和生成预训练(GPT)架构。为此,我们在综述策略中采用了一个循序渐进的过程:识别包含变换器的最新研究,应用过滤器提取最一致的研究,识别并定义包含和排除标准,评估每项研究中提出的策略,最后讨论所产生的文章中介绍的方法和架构。这些步骤有助于对基于 Transformer 架构的 NLP 应用进行系统总结和比较分析。主要重点是 NLP 领域的现状,特别是其应用、语言模型和数据集类型。研究结果让我们深入了解了这一研究领域所遇到的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Natural language processing with transformers: a review
Natural language processing (NLP) tasks can be addressed with several deep learning architectures, and many different approaches have proven to be efficient. This study aims to briefly summarize the use cases for NLP tasks along with the main architectures. This research presents transformer-based solutions for NLP tasks such as Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-Training (GPT) architectures. To achieve that, we conducted a step-by-step process in the review strategy: identify the recent studies that include Transformers, apply filters to extract the most consistent studies, identify and define inclusion and exclusion criteria, assess the strategy proposed in each study, and finally discuss the methods and architectures presented in the resulting articles. These steps facilitated the systematic summarization and comparative analysis of NLP applications based on Transformer architectures. The primary focus is the current state of the NLP domain, particularly regarding its applications, language models, and data set types. The results provide insights into the challenges encountered in this research domain.
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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