Natural language processing with transformers: a review.

Area of Science:

• Computer Science
• Artificial Intelligence
• Computational Linguistics

Background:

• Natural Language Processing (NLP) tasks are increasingly addressed using diverse deep learning architectures.
• Transformer-based models have emerged as highly efficient solutions for various NLP applications.

Purpose of the Study:

• To summarize the use cases and main architectures for NLP tasks.
• To present transformer-based solutions, specifically Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Training (GPT) architectures.
• To provide insights into the current state of the NLP domain, including applications, language models, and dataset types.

Main Methods:

• A systematic review strategy was employed, involving identifying recent Transformer studies.
• Filters were applied to select consistent studies, and inclusion/exclusion criteria were defined.
• Methods and architectures from selected articles were assessed and discussed.

Main Results:

• The review systematically summarized and comparatively analyzed NLP applications utilizing Transformer architectures.
• Key Transformer models such as BERT and GPT were highlighted for their efficiency in NLP tasks.
• Insights into current NLP applications, language models, and dataset types were generated.

Conclusions:

• Transformer architectures represent a significant advancement in addressing Natural Language Processing tasks.
• The study provides a foundational understanding of current NLP trends and challenges within the Transformer paradigm.
• Further research can build upon these findings to explore novel NLP applications and model improvements.