Design of an English translation system using convolutional neural networks based on biological mechanisms
Abstract
The application of neural network methods, especially convolutional neural networks (CNNs), has led to significant advances in machine translation technology. CNNs, inspired by the hierarchical organization and functional principles of biological systems, akin to how biomechanical structures adapt and respond, are able to effectively solve problems such as remote dependency and contextual nuances in language tasks, thus improving translation quality. In this study, multilayer CNN is introduced into neural machine translation (NMT), which significantly improves the BLEU score on the Chinese-English translation dataset. The optimal structure is a 6-layer CNN with 3 × 1 convolutional kernel, which performs well in context understanding. In terms of theoretical background, theories related to biological neural networks provide important insights. For example, biological neurons process information in a hierarchical structure to achieve decomposition and comprehension of complex tasks through feature extraction at different levels. CNNs mimic this biomechanically-inspired mechanism in language processing, employing convolutional layers to distill local traits and amalgamate them into comprehensive global knowledge. By exploring the successful mechanism of CNNs in language processing, this paper further reveals the transformative potential of neural hierarchical structures in computational linguistics, and opens up new paths for realizing more natural and accurate translation.
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