Research on Automatic Proofreading Algorithm for English Translation Based on Neural Networks

Abstract

In this proposed study, we explore the development and implementation of an innovative proofreading algorithm aimed at enhancing the accuracy of English translation. This algorithm leverages the capabilities of Convolutional Neural Networks (CNN) integrated with a fuzzy logic approach, offering a novel perspective in the realm of linguistic accuracy and consistency in translations. The core objective of this research is to address the prevalent challenges in automatic translation, such as context misinterpretation and semantic errors, by employing a fuzzy-based CNN model. This model is meticulously trained and tested using a diverse dataset of English translations, enabling it to learn and adapt to various linguistic nuances. Our results demonstrate a significant improvement in the proofreading accuracy, outperforming existing methods in terms of efficiency and reliability. The research highlights the potential of combining neural networks with fuzzy logic to create more sophisticated and context-aware translation tools. While our findings mark a considerable advancement in automatic translation proofreading, we also acknowledge the scope for further enhancements. Future work could involve refining the algorithm, expanding its applicability to other languages, and integrating it into real-world translation software. This research contributes to the evolving landscape of automated translation, presenting a promising solution for achieving higher translation fidelity.