Spring 2025-2026
Assoc. Prof. Dr. Alper Kumcu
Email | Website

Course Description

This course adopts a technology-oriented approach to translation practice and explores the role of Computer-Assisted Translation (CAT) tools in professional translation workflows. The primary tool used throughout the semester is Phrase TMS. Students will engage in hands-on activities designed to simulate real-world translation scenarios. The course covers translation memories (TM), terminology management (TB), quality assurance (QA), machine translation (MT), and post-editing processes. It also introduces code literacy for translators, structured file formats (HTML, XML, XLIFF), regular expressions (regex), and basic Natural Language Processing (NLP) concepts. In the second half of the semester, students will collaboratively translate a book using Phrase, applying project management principles, shared translation memories, terminology standardisation, and QA procedures. The course also critically addresses the ethical, economic, and professional implications of translation technologies and artificial intelligence in the translation industry.

Learning Outcomes

By the end of this course, students will be able to:

  • Explain the role of CAT tools and machine translation in professional translation workflows.
  • Create and manage translation projects in Phrase TMS, including translation memories and term bases.
  • Apply terminology management and quality assurance (QA) procedures to ensure consistency and accuracy.
  • Work with structured file formats (e.g., HTML, XML, XLIFF) and handle tag-related issues in translation environments.
  • Use regular expressions (regex) for text manipulation and batch editing tasks.
  • Demonstrate an understanding of neural machine translation and apply appropriate post-editing strategies.
  • Show basic awareness of Natural Language Processing (NLP) concepts relevant to translation practice.
  • Plan and execute a collaborative translation project using shared resources and workflow coordination.
  • Critically evaluate the ethical, economic, and professional implications of translation technologies.

Weekly Schedule

Week 1 – Course Introduction

Overview of course structure, expectations, assessment methods, and project framework. Introduction to the main tool (Phrase TMS) and semester workflow.

Week 2 – Overview of Translation Technologies & Workflow Logic

Definition of key concepts (TM, TB, MT, QA, segmentation).
Historical development and evolution of CAT tools and machine translation.
Professional roles in translation workflows and ISO 17100 standards.
Ethical and economic considerations in CAT and MT usage.
Discussion of current industry trends and challenges.

Week 3 – Translation Memory and Core CAT Mechanisms

Structure and functioning of translation memories.
Segmentation logic and fuzzy match calculations.
Alignment processes and data preparation.
Managing translation memories in Phrase.

Week 4 – Terminology Management and Quality Assurance (QA)

Importance of terminology standardisation.
Creating and managing term bases.
Consistency control and automated QA mechanisms.
Running and interpreting QA reports in Phrase.

Week 5 – Introduction to Coding for Translators

Structured file formats (TXT, HTML, XML, XLIFF).
Tags and markup logic.
Understanding segmentation and file structure.
Working with structured content in translation environments.

Week 6 – Regular Expressions (Regex) & Text Manipulation

Introduction to regex in translation workflows.
Batch editing and formatting standardisation.
Data cleaning and text manipulation techniques.

Week 7 – Artificial Intelligence, Machine Translation & Post-Editing

Basic principles of neural machine translation.
Types of post-editing (light vs. full).
Translation effort and quality considerations.
Critical discussion of AI-driven translation systems.

Week 8 – Midterm Exam

Assessment of theoretical knowledge and practical skills related to CAT workflows, terminology management, file handling, and machine translation.

Week 9 – Introduction to Natural Language Processing (NLP): Text Analysis and Data Awareness

Tokenisation and word frequency.
Basic text analysis concepts.
Understanding text as data.
Introductory applications relevant to translation practice.

Week 10 – Public Holiday

No class.

Week 11 – Project Phase 1

Project planning and task allocation.
Creating shared translation memories and term bases.
Initial translation of assigned sections.

Week 12 – Project Phase 2

Active collaborative translation.
Terminology standardisation and consistency management.
Workflow coordination within groups.

Week 13 – Project Phase 3

Running QA checks.
Peer revision and quality improvement.
Finalising translations for submission.

Week 14 – Project Submission and Reflection

Final delivery of the book translation project.
Group presentations and workflow reflection.
Discussion on the impact of translation technologies on professional identity and the future of the industry.

Evaluation

Midterm Exam (via HADİ – theoretical exam) – 30% Final Exam (Week 15, via HADİ – theoretical + practical components) – 50% Semester Project – Full workflow implementation of a realistic translation project using CAT tools – 10% Participation and Practical Work – In-class exercises and active participation – 10%

Important Regulations

  • Students who do not take the final exam will automatically receive F2 (fail).
  • To successfully pass the course, students must obtain at least 50/100 in the final exam. Students who score below 50/100 (i.e., below 25/50 within the final exam component) will automatically fail the course.
  • Attendance is compulsory. Students who exceed the absence limit will automatically receive F1 (fail).

Suggested Literature

Bowker, L. (2002). Computer-Aided Translation Technology: A Practical Introduction. University of Ottawa Press. O’Brien, S. (2012). Translation as human–computer interaction. Translation Spaces, 1(1), 101–122. Pym, A. (2013). Translation skill-sets in a machine-translation age. Meta, 58(3), 487–503. ISO 17100:2015. Translation services — Requirements for translation services. Kenny, D. (2017). Machine Translation for Everyone. Language Science Press.