Postgraduate Course: Machine Translation (Level 11) (INFR11062)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/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)
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Machine Translation (Level 10) (INFR10033)
|
Other requirements | Advanced Natural Language Processing or equivalent.
For Informatics PG and final year MInf students only, or by special permission of the School. |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2011/12 Semester 2, Not available to visiting students (SS1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | 17:10 - 18:00 | | | | | | Central | Lecture | | 1-11 | | | | 17:10 - 18:00 | |
| First Class |
Week 1, Monday, 17:10 - 18:00, Zone: Central. AT 2.12 |
| 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.
6 - Given typical MT translation problem students should be able to propose a range of different solutions to the problems and provide justified comparisons between their solutions.
7 - Students should also be able to identify the relationship between the given problem and others covered in the course. |
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 be close to research-level. |
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 40
Private Study/Other 40
Total 100 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk |
Course secretary | Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
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© Copyright 2011 The University of Edinburgh - 16 January 2012 6:17 am
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