Year: 2025
Type: article
Abstract: Ambiguous sentences in machine translation can lead to inaccurate or contextually inappropriate translations, which is a remarkable challenge. In this paper, we introduce a translation system that has a context-aware feature, and it is based on deep learning using transformer-type neural networks with attention mechanisms to amplify disambiguation. In particular, our model uses contextual embeddings and syntax-semantic analyses for model training to ensure translation accuracy, especially in lexical, syntactic, and referential ambiguity cases. We test our system against highly regarded translation systems and show that our model is capable of meaning preservation and fluency improvement. The experimental results show a remarkable performance upgrade, especially in translating low-resource and idiomatic texts. This study demonstrates how deep learning dynamically tailor's translation to context, improving disambiguation and fluency.
Author: Donghui Guo
Institution: Xi'an Fanyi University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410827033