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| ELL4036 | Machine Translation | 2+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | WESTERN LANGUAGES and LITERATURE (%100 English) | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Muhammed BAYDERE | | Co-Lecturer | | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | The course aims to provide a general understanding of the history, development, and current state of machine translation technology; to develop students? ability to use machine translation tools and integrate them into translation workflows; to build the knowledge and skills necessary for students to evaluate the quality of machine translation output and effectively carry out pre-editing and post-editing processes; to enable students to explore the ethical implications of machine translation, including issues such as data privacy, bias in machine learning, and the impact of machine translation on the translation profession; to help students develop a critical understanding of the strengths and limitations of machine translation and encourage them to adopt an informed and balanced approach to its use; to improve students? machine translation literacy; to expose students to the latest research and developments in the field of machine translation; and to prepare students for careers focusing on machine translation in the language services industry, technology companies, or academia. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Explain the history, development, and current state of machine translation technology. | 1 - 11 - 12 | 1, | | LO - 2 : | Use machine translation tools and effectively integrate them into translation workflows. | 11 | 1, | | LO - 3 : | Assess the quality of machine translation output and perform post-editing effectively. | 11 | 1, | | LO - 4 : | Discuss ethical issues related to machine translation, such as data privacy and bias. | 1 - 11 - 15 | 1, | | LO - 5 : | Critically evaluate the strengths and weaknesses of machine translation and demonstrate informed, responsible use of these technologies. | 11 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| The course focuses on the foundations of machine translation and its application areas/processes. It will primarily concentrate on post-editing, quality assessment, and ethical issues in machine translation. Within this course, in a context where machine translation is becoming increasingly complex and an integral part of global communication and all forms of digital media, content and practical activities will be provided to equip students with the knowledge, skills, and attitudes necessary to maintain a sustainable position in the ever-evolving field of machine translation. By combining theoretical learning with practical processes, students will learn how to benefit from the power of machine translation while also developing an understanding of the limitations and problematic aspects of these technologies. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction: Course overview, expectations, responsibilities, assessment | | | Week 2 | History and development of machine translation
| | | Week 3 | Readings on various aspects of machine translation
| | | Week 4 | General introduction to machine translation tools | | | Week 5 | Translation practice using machine translation tools | | | Week 6 | Machine translation post-editing: Techniques and best practices | | | Week 7 | Machine translation post-editing: Domain-specific text translation ? 1 | | | Week 8 | Machine translation post-editing: Domain-specific text translation ? 2 | | | Week 9 | Mid-term exam | | | Week 10 | Machine translation quality evaluation: Practice ? 1 | | | Week 11 | Machine translation quality evaluation: Practice-2 | | | Week 12 | Ethics in machine translation: Data privacy, bias, and its impact on the profession | | | Week 13 | Collaborative machine translation project based on human-machine interaction-1 | | | Week 14 | Collaborative machine translation project based on human-machine interaction-2 | | | Week 15 | General review | | | Week 16 | Final exam | | | |
| 1 | O Hagan, M. (Ed.). 2020; The Routledge Handbook of Translation and Technology, Routledge, Londra. | | | 2 | Moorkens, J., Castilho, S., Gaspari, F., & Doherty, S. (Ed.). 2018. Translation Quality Assessment: From Principles to Practice, Springer, Cham. | | | |
| 1 | Angelone, E., Ehrensberger-Dow, M., Massey, G. (Ed.). 2020; The Bloomsbury Companion to Language Industry Studies, Bloomsbury Publishing, Londra. | | | 2 | Munday, J. 2022; Introducing Translation Studies. Theories and Applications, Routledge, Londra. | | | 3 | Briva-Iglesias, V., O?Brien, S. 2022. The Language Engineer: A Transversal, Emerging Role for the Automation Age, Quaderns de Filologia: Estudis Lingüístics XXVII: 17-48. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 50 | | End-of-term exam | 16 | | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 2 | 14 | 28 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Arasınav için hazırlık | 2 | 8 | 16 | | Arasınav | 3 | 1 | 3 | | Dönem sonu sınavı için hazırlık | 3 | 7 | 21 | | Dönem sonu sınavı | 2 | 1 | 2 | | Diğer 1 | 4 | 2 | 8 | | Total work load | | | 120 |
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