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All Journal Jurnal Sumberdaya Hayati JMIE (Journal of Madrasah Ibtidaiyah Education) Awwaliyah Pendagogia: Jurnal Pendidikan Dasar Madani: Multidisciplinary Scientific Journal TROPICAL BIOSCIENCE: Journal of Biological Science Socius: Social Sciences Research Journal Jurnal Pengajaran Sekolah Dasar International Journal of Technology and Modeling
Asep Saefurohman
IAIN “SMH”Banten, Fakultas Tarbiyah dan Adab,Serang, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Revolutionizing Natural Language Processing (NLP): Cutting-edge Deep Learning Models for Chatbots and Machine Translation Arif, Muhamad; Saefurohman, Asep; Saluky
International Journal of Technology and Modeling Vol. 3 No. 1 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i1.111

Abstract

Natural Language Processing (NLP) has undergone a transformative evolution with the advent of deep learning, enabling significant advancements in chatbots and machine translation. This article explores state-of-the-art deep learning models, including Transformer-based architectures such as GPT, BERT, and T5, which have revolutionized the way machines understand and generate human language. We analyze how these models enhance chatbot interactions by improving contextual understanding, coherence, and response generation. Additionally, we examine their impact on machine translation, where neural models have surpassed traditional statistical approaches in accuracy and fluency. Despite these advancements, challenges remain, including computational costs, bias mitigation, and real-world deployment constraints. This article provides a comprehensive overview of recent breakthroughs, discusses their implications, and highlights future research directions in NLP-driven AI applications.
Co-Authors Ariyanti, Widi Az-Zahra, Seena Dewijanti, Indah Dwiatmi Fachry Abda El Rahman Iin Inayah, Iin Juhji, Juhji Khaeroni, Khaeroni Nurmala Sari Permatasari, Vera Prastya, Muhammad Eka Primahana, Gian Putri, Shiefa Aulia Putri, Yolanda RAMADAYANTI, DEVI Ridha, Rafifa Riskiana, Angger Arfi Saluky windarsih, gut Yusmia Nandini