Effortless and beneficial processing of natural languages using transformers

Title

Effortless and beneficial processing of natural languages using transformers

Creator

Amrutha K.; Prabu P.

Description

Natural Language Processing plays a vital role in our day-to-day life. Deep learning models for NLP help make human life easier as computers can think, talk, and interact like humans. Applications of the NLP models can be seen in many domains, especially in machine translation and psychology. This paper briefly reviews the different transformer models and the advantages of using an Encoder-Decoder language translator model. The article focuses on the need for sequence-to-sequence language-translation models like BERT, RoBERTa, and XLNet, along with their components. 2022 Taru Publications.

Source

Journal of Discrete Mathematical Sciences and Cryptography, Vol-25, No. 7, pp. 1987-2005.

Date

2022-01-01

Publisher

Taylor and Francis Ltd.

Subject

68T05; 68T50; ALBERTA; Attention model; BERT; GPT3; NLP; RoBERTa; Sequential model encoder-decoder

Coverage

Amrutha K., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 9720529

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1080/09720529.2022.2133239

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143499677&doi=10.1080%2f09720529.2022.2133239&partnerID=40&md5=57c51a912f413b0bf7ccc4e1774ee473