FI:PV061 Machine Translation Intro - Course Information
PV061 Introduction to Machine Translation
Faculty of InformaticsAutumn 2015
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
- Teacher(s)
- prof. PhDr. Karel Pala, CSc. (lecturer)
- Guaranteed by
- doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. PhDr. Karel Pala, CSc.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 12:00–13:50 C525
- Prerequisites
- Recommended are courses PA153 and Logical programming I
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Czech Language with Orientation on Computational Linguistics (programme FF, B-FI)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Informatics (eng.) (programme FI, D-IN4)
- Informatics (programme FI, D-IN4)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Technologies (eng.) (programme FI, D-IN4)
- Computer Systems and Technologies (programme FI, D-IN4)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Health Sciences (programme LF, N-SZ, specialization Teaching Specialization Optics and Optometrics)
- Image Processing (programme FI, N-AP)
- Course objectives
- The course offers the information about the basics of machine
translation. The students will learn about:
history of the Machine Translation (MT) and the state of art;
- approaches to MT: binary MT, interlingua based MT, techniques of translation memory using parallel corpora, statistical translation, factored translation and others;
- translation process: lexical analysis and machine dictionaries, morphological and syntactic analysis, representation of the sentence structure, transfer rules, semantic representation, synthesis;
- key issues of MT: ambiguity problem, knowledge representation, semantic issues, terminology;
The students will understand: - some successful MT systems: TAUM-METEO, TAUM-AVIATIC, EUROTRA, TRADOS, Dejavu, Rosetta, Google Translator, etc.;
- the existing systems working with Czech language - TRANSEN, PC-TRANSLATOR, Matrix;
- EU framework: projects like EuroMatrix, standardization, large parallel corpora, multipurpose and reusable resources;
- examples and experiments: small experimental translation system for Czech and English based on Prolog;
- machine translation and its relation to knowledge representation and Artificial Intelligence; - evaluation of the translation systems; - Syllabus
- History of the Machine Translation (MT) and the state of art;
- Approaches to MT: binary MT, interlingua based MT, techniques of translation memory using parallel corpora, statistical translation, factored translation;
- Translation process: lexical analysis and machine dictionaries, morphological and syntactic analysis and representation of sentence structure, transfer rules, semantic representation, synthesis;
- Key issues of MT: ambiguity problem, knowledge representation, semantic issues, terminology;
- MT with speech input and output (Verbmobil);
- Some successful MT systems: TAUM-METEO, TAUM-AVIATIC, EUROTRA, TRADOS, Dejavu, Rosetta, Google Translator the existing systems working with Czech language - TRANSEN, PC-TRANSLATOR, Matrix;
- EU framework: projects like EuroMatrix, standardization, large parallel corpora, multipurpose and reusable resources;
- Examples and experiments: small experimental translation system in Prolog - Czech - English;
- Evaluation of the translation systems;
- Machine Translation and its relation to Artificial Intelligence;
- Literature
- HUTCHINS, W. John and Harold L. SOMERS. An introduction to machine translation. London: Academic Press, 1992, xxi, 362 s. ISBN 0-12-362830-X. info
- Teaching methods
- Teaching is performed in the form of oral lectures and seminars, in which the slides and demos of the relevant software tools are combined. Students work out homeworks, prepare presentations based on the literature they had read and develop smaller projects. At the appropriate points of the teaching the open dialog between a teacher and students is used.
- Assessment methods
- oral examination, written test
- Language of instruction
- Czech
- Further Comments
- Study Materials
The course is taught once in two years.
- Enrolment Statistics (Autumn 2015, recent)
- Permalink: https://is.muni.cz/course/fi/autumn2015/PV061