TY - GEN
T1 - Neural Network Architectures for Machine Translation
T2 - 6th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2024
AU - Ayushi,
AU - Kumar, Sudhakar
AU - Singh, Sunil K.
AU - Singh, Samar Pratap
AU - Rai, Pooja
AU - Chui, Kwok Tai
AU - Gupta, Brij B.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - The challenge of providing quality education to all, as outlined in SDG 4, is compounded by language barriers that hinder access to educational resources. This paper addresses this problem by exploring advancements in neural network architectures for machine translation. It covers key technologies such as Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and the transformative Transformer model. Through a detailed literature review and algorithmic insights, the paper provides a comprehensive understanding of these methods. Examining case studies and experimental results, this paper highlights the impact of improved machine translation on educational accessibility for non-native speakers and multilingual regions. The advancements enable more accurate and relevant translations, enhancing learning outcomes. This research underscores the potential of machine translation to democratize education, showcasing it a powerful tool for achieving SDG 4 and fostering inclusive, equitable education globally.
AB - The challenge of providing quality education to all, as outlined in SDG 4, is compounded by language barriers that hinder access to educational resources. This paper addresses this problem by exploring advancements in neural network architectures for machine translation. It covers key technologies such as Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and the transformative Transformer model. Through a detailed literature review and algorithmic insights, the paper provides a comprehensive understanding of these methods. Examining case studies and experimental results, this paper highlights the impact of improved machine translation on educational accessibility for non-native speakers and multilingual regions. The advancements enable more accurate and relevant translations, enhancing learning outcomes. This research underscores the potential of machine translation to democratize education, showcasing it a powerful tool for achieving SDG 4 and fostering inclusive, equitable education globally.
KW - Convolutional Neural Networks (CNNs)
KW - Deep Learning
KW - Gated Recurrent Units (GRUs)
KW - Long Short-Term Memory Networks (LSTMs)
KW - Machine Translation
KW - Natural Language Processing (NLP)
KW - Neural Network Architectures
KW - Recurrent Neural Networks (RNNs)
KW - Transformative Transformer model
UR - https://www.scopus.com/pages/publications/105005932575
U2 - 10.1007/978-3-031-88039-1_18
DO - 10.1007/978-3-031-88039-1_18
M3 - Conference contribution
AN - SCOPUS:105005932575
SN - 9783031880384
T3 - Communications in Computer and Information Science
SP - 223
EP - 238
BT - Soft Computing and Its Engineering Applications - 6th International Conference, icSoftComp 2024, Revised Selected Papers
A2 - Patel, Kanubhai K.
A2 - Santosh, KC
A2 - Gomes de Oliveira, Gabriel
A2 - Patel, Atul
A2 - Ghosh, Ashish
Y2 - 10 December 2024 through 12 December 2024
ER -