THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2012/2013
- ARCHIVE for reference only
THIS PAGE IS OUT OF DATE

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Machine Translation (Level 10) (INFR10033)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website http://www.inf.ed.ac.uk/teaching/courses/mt Taught in Gaelic?No
Course descriptionMachine Translation deals with computers translating human languages (for example, from Arabic to English). The field is now sufficiently mature that Google use it to allow millions of people to translate Web Documents each day. This course deals with all aspects of designing, building and evaluating a range of state-of-the-art translation systems. The systems covered are largely statistical and include: word-based, phrase-based, syntax-based and discriminative models. As well as exploring these systems, the course will cover practical aspects such as using very large training sets, evaluation and the open problem of whether linguistics can be useful for translation.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Foundations of Natural Language Processing (INFR09028)
Co-requisites
Prohibited Combinations Students MUST NOT also be taking Machine Translation (Level 11) (INFR11062)
Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2012/13 Semester 2, Available to all students (SV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 16:10 - 17:00
CentralLecture1-11 16:10 - 17:00
First Class Week 1, Monday, 16:10 - 17:00, Zone: Central. B1, Forrest Hill
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Summary of Intended Learning Outcomes
1 - Provide a written description of the main algorithms used in the system.
2 - Design and justify an approach to the evaluation of the system using state of the art tools and metrics
3 - Analyse the data collected by such an evaluation.
4 - Where the system is designed to deal with large volumes of data the student should also be able to describe how the system handles large data volumes and critically compare the system?s solution with other common solutions to the problem.
5 - Identify where linguistics knowledge is relevant in the design of the system and what influence of linguistic knowledge has on the translation quality and performance of the system.
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0

Assessment
A single piece of coursework dealing with some practical aspect of translation. This will involve analysis of particular problems (for example, reordering or word selection), as well as actual problem solving. Typically, the problems set will not be research-level.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus History of MT
* rule-based systems, ALPAC report, IBM models, phrase-based systems

Models
* word-based
* phrase-based
* syntax-based
* discriminative
* Factored Models

Reordering
* Lexicalised reordering
* Distortion
* Changing the source

Language Modelling
* Ngram models
* Scaling LMs (cluster-based LMs, Bloom Filter LMs)

Decoding
* Knight on complexity, problem statement
* Stack decoding

Evaluation
* Human evaluation
* Automatic methods
* NIST competitions

Adding linguistics
* Reranking
* As factors

Parallel corpora etc (data)
* What they are, where they come from
* Comparable corpora
* Multi-parallel corpora

Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Human-Computer Interaction (HCI), Natural Language Computing
Transferable skills Not entered
Reading list * Philipp Koehn: Statistical Machine Translation (forthcoming)
* Primary literature
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 35
Private Study/Other 45
Total 100
KeywordsNot entered
Contacts
Course organiserDr Mary Cryan
Tel: (0131 6)50 5153
Email: mcryan@inf.ed.ac.uk
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Timetab
Prospectuses
Important Information
 
© Copyright 2012 The University of Edinburgh - 14 January 2013 4:09 am