Enhancing Biotechnological Communication and Collaboration: The Integration of Computer-Assisted Translation and Artificial Intelligence

Abstract

This paper presents the development of a novel algorithm using the Natural Language Toolkit (NLTK) and neural network architecture to enhance translation processes in the biotechnology sector. Our model, trained on 10,000 parallel sentences (English to French and vice versa), leverages deep learning techniques, specifically through the use of Keras and the BLEU score metric for evaluating translation accuracy. By adopting a 90:10 split for training and testing datasets, we ensure rigorous assessment of the model’s performance. The rapid advancement of information technology has revolutionized the language services industry, making computer-assisted translation an indispensable tool in global biotechnological communications. This study not only demonstrates the practical application of these technologies in improving translation efficiency but also explores their impact on the dissemination of biotechnological information across linguistic barriers. By integrating AI with CAT tools, we aim to facilitate more effective international collaboration and knowledge transfer in the biotech field. Our findings indicate that the application of advanced machine translation models can significantly enhance the accuracy and efficiency of translations in biotechnology. This breakthrough has important implications for improving global communication in biotechnological research and commercial activities, suggesting a future where AI-powered translation tools are central to innovation and collaboration in the industry.