Module: Machine Translation

Module Specifications..

Current Academic Year 2022 - 2023

Please note that this information is subject to change.

Module Title	Machine Translation
Module Code	CA4012
School	School of Computing
Module Co-ordinator	Semester 1: Kolawole John Adebayo Semester 2: Kolawole John Adebayo Autumn: Kolawole John Adebayo
Module Teachers	Andrew Way John McKenna Brian Davis Maja Popovic Kolawole John Adebayo
NFQ level	8	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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.

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

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.

Assessment Breakdown
Continuous Assessment	30%	Examination Weight	70%

Course Work Breakdown
Type	Description	% of total	Assessment Date
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

Reassessment Requirement Type
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
This module is category 1

Indicative Reading List

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

Other Resources

None

Array

Programme or List of Programmes

CASE

BSc in Computer Applications (Sft.Eng.)

ECSA

Study Abroad (Engineering & Computing)

ECSAO

Study Abroad (Engineering & Computing)

Timetable this semester: Timetable for CA4012

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