Registry

Module Specifications

Archived Version 2019 - 2020

Module Title
Module Code
School
Online Module Resources
NFQ level	8	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

This course introduces the fundamentals of statistical machine translation.

Learning Outcomes

1. Discuss the challenges associated with machine translation
2. Explain the noisy channel model underpinning statistical machine translation
3. Demonstrate how a statistical translation model can be inferred from a parallel corpus of texts using unsupervised machine learning techniques
4. Explain the concept of statistical language modelling and how it fits in to the basic SMT architecture
5. Explain the concept of decoding and be in a position to implement a beam decoder
6. Evaluate a statistical machine translation system using at least one automatic metric
7. Demonstrate a knowledge of the state-of-the-art in statistical machine translation
8. Train, test and evaluate MT systems using the open-source Moses toolkit
9. Implement a language modeller (including smoothing) and a basic word aligner

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	24	Two lectures a week
Laboratory	24	One two-hour lab session a week
Group work	40	Group project
Assignment Completion	50	Individual assignment
Independent Study	50	Studying material presented in lecture, reading research papers
Total Workload: 188

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Noisy Channel Model of Statistical Machine Translation
The noisy channel model and its link to Bayes Theorem

Evaluating SMT systems
The relative advantages and disadvantages of human evaluation, automatic evaluation and task-based evaluation. The BLEU evaluation metric

Language Modelling
The role of language modelling in SMT. The importance of smoothing in language modelling

Translation Models
Learning a word-based translation model from a parallel corpus using Expectation Maximization. Deriving a phrase-based model from a word-based model.The relative strengths and weaknesses of various models

Decoding
A beam search decoding algorithm for SMT. Techniques for pruning the search space.

Encoding Linguistic Information in an SMT system
Techniques for including morphological, syntactic and semantic knowledge in an SMT system

Assessment Breakdown
Continuous Assessment	%	Examination Weight	%

Course Work Breakdown
Type	Description	% of total	Assessment Date

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
Unavailable

Indicative Reading List

Philipp Koehn,: 0, Statistical Machine Translation, 0521874157

Other Resources

None

Programme or List of Programmes

Timetable this semester: Timetable for

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