| Objectives: |
The aim of this course is to enable students to understand the basic working principles, opportunities, and limitations of artificial intelligence and machine translation technologies, and to develop their skills in evaluating translation outputs, post-editing, and effective prompt design. The course also aims to raise awareness of the ethical, legal, and professional dimensions of AI use and the changing role of the translator. |
| Content: |
This course examines the historical, theoretical, and technological development of machine translation through a chronological approach, from the earliest ideas of mechanical translation to contemporary artificial intelligence and large language model-based systems. The course covers the emergence of machine translation, early attempts at automatic translation, rule-based machine translation, direct translation, transfer-based translation, the interlingua approach, example-based machine translation, corpus-based methods, statistical machine translation, hybrid systems, neural machine translation, attention mechanisms, Transformer architecture, multilingual systems, large language models, and generative AI applications.
Students learn the fundamental working principles of each machine translation approach, the linguistic or numerical data they rely on, their strengths and weaknesses, common error types, and their impact on translators’ workflows. Outputs produced by systems developed in different periods are compared using the same or similar texts in terms of accuracy, fluency, terminology, context, consistency, cultural appropriateness, and suitability for text type.
The course also covers the integration of machine translation with computer-assisted translation tools, the use of translation memories and terminology databases, source-text pre-editing, the post-editing of machine translation output, and the evaluation of translation quality through both human and automatic methods.
The final part of the course focuses on the use of large language models and generative AI systems in translation. Students carry out practical activities involving translation-oriented prompt design, providing contextual and terminological information, generating alternative translations, verifying AI-generated outputs, and designing translation workflows based on human–AI collaboration.
In addition, the course addresses ethical and legal issues related to the use of artificial intelligence and machine translation, including confidentiality, data security, protection of personal data, copyright, data ownership, algorithmic bias, hallucinated information, transparency, academic integrity, and the professional responsibility of translators. |