Edge NLP for Efficient Machine Translation in Low Connectivity Areas

Watt, Tess; Chrysoulas, Christos; Gkatzia, Dimitra

Abstract

Machine translation (MT) usually requires connectivity and access to the cloud which is often limited in many parts of the world, including hard to reach rural areas. Edge natural language processing (NLP) aims to solve this problem by processing language data closer to the source. To achieve this, 100 sentence pairs were stored and processed on a Raspberry Pi, and a recurrent neural network (RNN) using the long short-term memory (LSTM) architecture was used for machine translation. We are focusing on translating between English and Hausa, a low-resource language spoken in West Africa. It was found that the developed prototype produced "good and fluent translations" with a training accuracy of 91%. The model also achieved a BLEU score of 73.5, compared to the existing models that have scores of 22.2 and below.

Citation

Watt, T., Chrysoulas, C., & Gkatzia, D. (2023, October). Edge NLP for Efficient Machine Translation in Low Connectivity Areas. Presented at IEEE 9th World Forum on Internet of Things: 2nd Workshop on Convergence of Edge Intelligence in IoT (EdgeAI-IoT 2023), Aveiro, Portugal

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE 9th World Forum on Internet of Things: 2nd Workshop on Convergence of Edge Intelligence in IoT (EdgeAI-IoT 2023)
• Start Date: Oct 12, 2023
• End Date: Oct 27, 2023
• Acceptance Date: Sep 11, 2023
• Online Publication Date: May 30, 2024
• Publication Date: 2023
• Deposit Date: Dec 2, 2023
• Publicly Available Date: Dec 31, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Book Title: 2023 IEEE 9th World Forum on Internet of Things (WF-IoT)
• ISBN: 9798350311624
• DOI: https://doi.org/10.1109/WF-IoT58464.2023.10539577
• Keywords: edge computing, computation offloading, artificial intelligence, machine learning
• Public URL: http://researchrepository.napier.ac.uk/Output/3402802
• Related Public URLs: https://wfiot2023.iot.ieee.org/