(untitled) (Ustozlar uchun, 2026)
Machine Translation (MT) has become an integral tool in bridging language barriers, driven by advancements in statistical and neural network-based technologies. Modern MT systems, particularly Neural Machine Translation (NMT), have demonstrated significant improvements in fluency, speed, and scalability, making them valuable for real-time communication, multilingual content generation, and global business operations. Despite these advancements, MT systems continue to face inherent limitations. Challenges include the accurate translation of idiomatic expressions, culturally nuanced content, domain-specific terminology, and low-resource languages. Additionally, MT output often lacks the contextual understanding, creativity, and cultural sensitivity that human translators provide. Ethical and practical concerns, such as data privacy, bias in training datasets, and overreliance on automated tools, further highlight the constraints of MT. This article examines the current state of machine translation, its operational strengths, and the limitations that restrict its ability to fully replace human expertise, emphasizing the need for collaborative human-machine approaches to achieve high-quality translation.