COURSE SYLLABUS

MACHINE TRANSLATION

1	Course Title:	MACHINE TRANSLATION
2	Course Code:	BM6040
3	Type of Course:	Optional
4	Level of Course:	Third Cycle
5	Year of Study:	1
6	Semester:	2
7	ECTS Credits Allocated:	6
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Doç. Dr. Metin BİLGİN
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	Bilgisayar Müh. Bölüm Binası, 1. kat, oda 109 Tel.:+90 (224) 275 52 63 email: metinbilgin at uludag.edu.tr
18	Website:
19	Objective of the Course:	Machine translation is an important subfield of Natural Language Processing and deals with various usage possibilities of computers in the process of text or speech translation from one language to another. Besides fully automatic translation systems, tools for helping human translators are also developed in this context. This course aims to cover the usage of computer systems in the translation process, to teach the modern approaches and tools for the state-of-the-art machine translation, to investigate the details of the current machine translation methods.
20	Contribution of the Course to Professional Development	Engineering Science: 85%; Engineering Design: 15%

Learning Outcomes:

1	Students will learn how to use computers during natural language translation process. ;
2	Students will learn the methods for evaluating the performance machine translation system outputs. ;
3	Students will learn transfer based and example based machine translation systems. ;
4	Students will learn how to apply statistical methods to machine translation. ;
5	Students will learn basic knowledge about spoken language translation. ;
6	Students will gain knowledge about the machine translation methods that can be used for cognate or resource poor languages. ;
7	Students will have a broad knowledge about state-of-the-art and commercial machine translation systems. ;

22	Course Content:

Week	Theoretical	Practical
1	Introduction, History of Machine Translation
2	Recent Methods and Theory for Machine Translation
3	Evaluation of Machine Translation
4	Human Evaluation for Machine Translation Outputs
5	Transfer Based Machine Translation Methods (I)
6	Transfer Based Machine Translation Methods (II) Interlingua Based Methods
7	Example Based Translation Methods
8	Statistical Machine Translation (I) Introduction, Language Model Component
9	Statistical Machine Translation (II) Translation Model
10	Statistical Machine Translation (III) Decoding
11	Phrase Based Statistical Machine Translation Incorporating Syntax in Statistical Machine Translation
12	Speech-to-Speech Translation
13	Machine Translation Between Related Languages Machine Translation for Resource Poor Languages
14	Commercial Machine Translation Systems

23	Textbooks, References and/or Other Materials:	Koehn, P. 2010. Statistical Machine Translation, Cambridge University Press. Trujillo, A., 1999. Translation Engines : Techniques for Machine Translation, Springer-Verlag Series on Applied Computing. Hutchins, W. J., Somers H.L., 1992. An Introduction to Machine Translation, Academic Press, San Diego. Nirenburg, S., Somers H.L., Wilks, A. Y., 2002. Readings in Machine Translation, The MIT Press, Cambridge Manning, C. D. and Schütze, H., 1999. Foundations of Statistical Natural Language Processing, The MIT Press, Cambridge.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	0	0
Quiz	0	0
Homeworks, Performances	3	40
Final Exam	1	60
Total	4	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	3 Project/Homework (%40) and 1 Final Exam (%60)
Information

ECTS / WORK LOAD TABLE

CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High