Machine Translation (Level 10) (U04176)

? Credit Points : 10 ? SCQF Level : 10 ? Acronym : INF-4-MT-4

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

? Pre-requisites : Foundations of Natural Language Processing

? Prohibited combinations : Machine Translation (Level 11)

Subject Areas

Home subject area

Computational Linguistics, (School of Informatics, Schedule O)

Other subject areas

Cognitive Science, (School of Informatics, Schedule O)

Artificial Intelligence, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : 4th year

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
12/01/2009	17:10	18:00	Room 4.12, Appleton Tower	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	17:10	18:00	Central
Lecture	Thursday	17:10	18:00	Central

Summary of Intended Learning Outcomes

Given a MT system (or a description of an MT system) students should be able to:
- Provide a written description of the main algorithms used in the system.
- Design and justify an approach to the evaluation of the system using state of the art tools and metrics
- Analyse the data collected by such an evaluation.

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.

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.