Semantic communication has emerged as a promising paradigm for bandwidth-constrained or error-prone environments by focusing on the transmission of meaningful representations rather than raw data. In this paper, we propose a novel framework for enabling language translation in semantic communication, allowing end-users to communicate seamlessly in different languages. Specifically, we design a Transformer-based model to facilitate translation between transmitters and receivers. To enhance efficiency without compromising accuracy, we integrate a knowledge distillation technique using a teacher-student architecture to optimize model performance. To evaluate the effectiveness of our framework under realistic conditions, we conduct experiments on an English-Vietnamese dataset through simulated communication channels. Experimental results demonstrate that our approach outperforms conventional communication methods across various simulated environments, highlighting its potential for practical deployment in multilingual semantic communication systems.