The paper presents a neural machine translation system with a context-sensitive mechanism. It uses a recursive self-encoder to learn the representations of English sentences, which are then contextualized by topic distribution. Furthermore, the parameters of the model are optimized by applying a deep reinforcement learning algorithm, which enhances the accuracy of the model further. Experiments are performed on different datasets to assess the performance of the proposed model. It was found that the model exhibits its highest level of translation performance at the cosine similarity threshold of 0.9, and that there are notable improvements in the quality of translations following fine-tuning of the parameters utilizing deep reinforcement learning. Bleu scores increase by 2.00 – 4.84 points, which indicates the efficiency of the fine-tuning stage. Besides, the incorporation of the context-sensitive bilingual-constrained recursive autocoder boosts the bleu scores up to 5.23 – 7.76 points over the baseline and variant models. On the whole, the addition of deep reinforcement learning and the context-sensitive method makes the model much more effective at producing English translations that are correct.
