Postgraduate Course: Machine Translation (Level 11) (INFR11062)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://course.inf.ed.ac.uk/mt |
Taught in Gaelic? | No |
| Course description | Machine 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 |
It is RECOMMENDED that students have passed
Advanced Natural Language Processing (INFR11059) OR
Foundations of Natural Language Processing (INFR09028)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
Advanced Natural Language Processing or equivalent. |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2014/15 Semester 2, Available to all students (SV1)
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Learn enabled: No |
Quota: None |
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Web Timetable |
Web Timetable |
| Course Start Date |
12/01/2015 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Machine Translation | 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
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.
You should expect to spend approximately 40 hours on the coursework for this course.
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 |
* Statistical Machine Translation, P. Koehn, Cambridge University Press, 2010
* Primary literature
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| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Mary Cryan
Tel: (0131 6)50 5153
Email: mcryan@inf.ed.ac.uk |
Course secretary | Miss Claire Edminson
Tel: (0131 6)51 7607
Email: C.Edminson@ed.ac.uk |
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