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Natural language processing with transformers: a review
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DOI：10.7717/peerj-cs.2222.png)
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摘要
En 中文
Natural language processing (NLP) tasks can be addressed with several deep learning architectures, and many different approaches have proven to be efficient. fi cient. This study aims to briefly fl y 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 stepby-step process in the review strategy: identify the recent studies that include Transformers, apply fi lters to extract the most consistent studies, identify and define fi ne inclusion and exclusion criteria, assess the strategy proposed in each study, and fi nally 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.
Keyword:
Transfomers
Natural language processing
Deep neural network architectures
Review
Trends
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期刊
IF:
2.5
论文数:
3.4K
被引数:
6.9K
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分享收藏An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools自然语言处理中的深度学习: 模型、技术和工具.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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