Construction and Evaluation of Context Aware Machine Translation System

With the deepening of globalization, the demand for cross-language communication is increasing. Traditional machine translation (MT) systems often have unstable translation quality due to their lack of context sensitivity. To meet this challenge, this paper proposes a context-aware machine translation system, which aims to improve the accuracy and fluency of translation by dynamically capturing and understanding the context information in the translation task. First, this paper introduces a context-aware mechanism and adopts the sequence-to-sequence model (Seq2Seq) and self-attention mechanism in deep learning to enhance the model’s understanding of text contextual relationships. Then, pre-training is combined with a large-scale bilingual corpus to enable the system to better capture the correlation between vocabulary and syntactic structure. Then, this paper combines the context information to effectively determine polysemous words and ambiguous sentences in the translation process. Finally, this paper uses an adaptive adjustment mechanism to enable the system to adjust the translation output in real time. Through experimental verification on multiple corpora, the results show that the model performs significantly better than traditional Seq2Seq and Transformer models in translating long sentences, complex sentences and polysemous words. Specifically, the context-aware translation system achieved a BLEU (Bilingual Evaluation Understudy) score of 0.51 when translating long sentences. In terms of translating polysemous words and handling ambiguous sentences, the context-aware system effectively reduced polysemous word errors and ambiguous sentence processing errors. These experimental results show that the context-aware translation system has significant advantages in improving translation quality and context adaptability.