Module: Machine Translation

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Machine Translation
Module Code	CSC1105 (ITS: CA4012)
Faculty	Computing	School	Engineering & Computing
NFQ level	8	Credit Rating	7.5

Description

This course introduces the fundamentals of machine translation, including the currently widely used neural approach.

Learning Outcomes

1. Discuss the challenges associated with machine translation including its evaluation.
2. Explain the concept of machine translation including approaches and the importance of language data.
3. Demonstrate how a statistical translation model can be inferred from a parallel corpus of texts using unsupervised machine learning techniques.
4. Explain how neural networks work in general and how they can be used for language-related tasks.
5. Explain the concepts of statistical language modelling and neural language modelling and their differences.
6. Explain the decoding process in NMT and understand the differences between decoding in SMT and decoding in NMT.
7. Demonstrate a knowledge of the state-of-the-art transformer neural machine translation.
8. Explain the differences between recurrent machine translation and transformer machine translation.
9. Train, test and evaluate MT system using the open-source Joey NMT tookit.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
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

Section Breakdown
CRN	20393	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Ellen Rushe	Module Teacher	Andrew Way (Emeritus Prof), Brian Davis, John McKenna, Kolawole John Adebayo, Maja Popovic

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Students undertake a group project of their choosing which involves training a machine translation system using the open-source toolkit Joey NMT which was developed for educational purposes.	10%	Once per semester
Assignment	Students take on a significant individual project which involves calculations related to 1) automatic evaluation methods, 2) language model probabilities, 3) translation model probabilities 4) neural networks.	20%	Once per semester
Formal Examination	End-of-Semester Final Examination	70%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

Introduction to Machine Translation
What is machine translation? Overview of the three approaches: rule-based, statistical, neural. Importance of data for statistical and neural MT. Sentence alignment and preprocessing.

Evaluating MT systems
The relative advantages and disadvantages of human evaluation and automatic evaluation. Two main concepts used for automatic evaluation metrics: n-gram matching and edit distance.

Statistical Machine Translation
Probability model for translation, Translation model and Language model, Word Alignments and IBM models, Phrase-based SMT, Decoding.

Introduction to Neural Networks
What are neural networks? Architectures: feed forward and recurrent networks. Training neural networks: back-propagation and gradient descent.

Neural Language Models
Word representations: why are they needed? Different types: one-hot, static, contextual, external vs internal representations. Feed-forward neural language models. Recurrent neural language models.

Neural Machine Translation
Encoder-decoder architecture and sequence-to-sequence modelling. Decoding for NMT. Recurrent neural networks for MT. Recurrent neural MT with attention. Transformer neural networks for MT.

Indicative Reading List

Books:

Philipp Koehn,: 0, Statistical Machine Translation, 0521874157
Philipp Koehn: 0, Neural Machine Translation, 9781108608480

Articles:
None

Other Resources

None