Errors in neural networks and translation programs as a basis for expanding the training opportunities of translators

Object: This study investigates the errors produced by neural network translation systems and explores how these errors can be leveraged to enhance translator training. It addresses the increasing role of machine translation in the translation landscape and the importance of equipping future translators with the skills to critically evaluate and effectively utilize these technologies. Methods: The research combines an overview of existing literature on machine translation errors and translator training methodologies with a practical analysis of machine translation outputs. A specific article (“Compressor training courses: a positive for over 10 years of continuing improvement”) is used as a case study. The study compares the pre-translation analysis and the translation of this article generated by different neural network translation platforms (ChatGPT, DeepL, Google Translate, Yandex Translate) with human analysis. The identified discrepancies and errors are then examined for their potential use in developing targeted training exercises. Findings: The analysis reveals that neural network translation systems still struggle with contextual understanding, nuanced language, and accurate rendering of specialized terminology, particularly in texts blending scientific, technical, and promotional elements. The systems often produce inaccurate pre-translation analyses, stylistic inconsistencies, word choice errors, and fail to capture the intended meaning and tone of the source text. However, the study demonstrates that these errors, when carefully analyzed, can serve as valuable learning material for translator trainees. By identifying and correcting these errors, students can develop critical thinking skills, improve their understanding of source and target language nuances, and hone their ability to make informed translation decisions. For example, neural networks often miss the appropriate register and fail to recognize industry specific terms, or even suggest the wrong language registers. Conclusion: The study concludes that while machine translation tools are becoming increasingly sophisticated, they are not yet capable of replacing human translators. Instead, the future of the translation profession lies in the collaboration between human expertise and technological tools. By incorporating the analysis of machine translation errors into translator training programs, educators can equip students with the skills necessary to effectively utilize these tools, critically evaluate their output, and deliver high-quality translations that meet the demands of an increasingly globalized world. A balanced approach is advocated, emphasizing both the use of digital tools and the development of critical text comprehension skills, particularly in specialized domains.