The rapid development of artificial intelligence (AI) has significantly impacted the translation industry. AI-powered translation systems, such as neural machine translation (NMT), have enabled faster and more cost-effective translations compared to traditional human translators. However, AI translations often lack contextual understanding, cultural sensitivity, and stylistic nuances. This paper aims to provide a comparative analysis of AI and human translation, evaluating their respective strengths and weaknesses. The study synthesizes findings from recent research and empirical observations to explore the effectiveness, reliability, and applicability of AI translation tools in different linguistic and professional contexts.

Artificial intelligence, human translation, neural machine translation, translation quality, linguistic accuracy, cultural context.

Vaswani, A., et al. “Attention is All You Need.” Neural Information Processing Systems, 2017, pp. 5998–6008.

Koehn, P. Statistical Machine Translation. Cambridge University Press, 2020, pp. 34–67.

Toral, A., et al. “Neural Machine Translation vs. Human Translation: Comparative Evaluation.” Journal of Artificial Intelligence Research, 2018, pp. 45–78.

Wu, Y., et al. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.” arXiv preprint, 2016, pp. 1–15.

Tiedemann, J. Neural Machine Translation and Translation Memory Integration. Springer, 2019, pp. 101–140.

Crego, J., et al. “Systran Neural Machine Translation Systems.” Machine Translation, 2016, pp. 111–120.

Zhang, J., et al. “Context-aware Neural Machine Translation.” Transactions of the Association for Computational Linguistics, 2020, pp. 271–285.

Popović, M. “Assessing Quality of Machine Translation Output.” Language Resources and Evaluation, 2017, pp. 1–20.

Graham, Y., et al. “Evaluating the Accuracy of AI Translators in Technical Domains.” Computational Linguistics Journal, 2019, pp. 203–223.

Pym, A. Exploring Translation Theories. Routledge, 2018, pp. 58–93.

Hovy, E., et al. “The Role of Context in Machine Translation.” AI Magazine, 2019, pp. 67–75.

Sennrich, R., et al. “Improving Neural Machine Translation Models with Monolingual Data.” ACL Conference Proceedings, 2016, pp. 86–96.