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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Natural Language Understanding, Generation, and Machine Translation (INFR11157)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
Summary**This course replaces Natural Language Generation (Level 11) (INFR11060), Machine Translation (Level 11) (INFR11062) and Natural Language Understanding (Level 11) (INFR11061).**

This course explores current research on processing natural language: interpreting, generating, and translating. The course will focus mainly on deep learning approaches to various NLP tasks and applications. It will provide an introduction to the main neural network architectures used in NLP and how they are used for tasks such as syntactic and semantic parsing, as well as end-user applications such as machine translation and text summarization.
Building on linguistic and algorithmic knowledge taught in prerequisite courses, this course also aims to further develop students' understanding of the strengths and weaknesses of current approaches with respect to linguistic and computational considerations. Practical assignments will provide the opportunity to implement and analyse some of the approaches considered.
Course description The course aims to familiarize students with recent research across a range of topics within NLP, mainly within the framework of neural network models, and with a focus on applications such as machine translation, summarisation, and semantic parsing. As an MSc-level course that assumes previous experience with NLP, it will discuss a range of different issues, including linguistic/representational capacity, computational efficiency, optimization, etc. There is no textbook for the course; readings will come from recent research literature.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Accelerated Natural Language Processing (INFR11125) OR Informatics 2A - Processing Formal and Natural Languages (INFR08008) AND Foundations of Natural Language Processing (INFR09028)
Co-requisites Students MUST also take: Introductory Applied Machine Learning (INFR10069) OR Machine Learning and Pattern Recognition (INFR11130) OR Machine Learning Practical (INFR11132)
Prohibited Combinations Other requirements Programming skills at least at the level of Computer Programming for Speech and Language Processing are also required. This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your Degree Programme Table (DPT), please seek special permission from the course organiser.
Information for Visiting Students
Pre-requisitesProgramming skills at least at the level of Computer Programming for Speech and Language Processing are also required. This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your Degree Programme Table (DPT), please seek special permission from the course organiser.
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 30, Supervised Practical/Workshop/Studio Hours 3, Feedback/Feedforward Hours 2, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 159 )
Assessment (Further Info) Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Additional Information (Assessment) Written exam 70%
Coursework 30%
Practical Exam 0%
Feedback Some of the lecture time will be devoted to discussing questions, including some exam-like questions, and providing feedback on student answers. Students will also get feedback on their work through labs.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Identify and discuss the main linguistic, machine learning, and ethical challenges involved in the development and use of natural language processing systems.
  2. Understand and describe state-of-the-art models and algorithms used to address challenges in natural language processing systems.
  3. Design, implement, and apply modifications to state-of-the-art natural language processing systems.
  4. Understand the computational and engineering challenges that arise in the use of different models for natural language processing, and discuss the pros and cons of different models for a given task.
  5. Understand, design and justify approaches to evaluation and error analysis in natural language processing systems.
Reading List
There is no textbook for the course; readings will come from recent research literature.
Additional Information
Graduate Attributes and Skills Students will develop their skills in reading research papers and identifying pros and cons of different approaches. They will also learn to analyze and discuss results from their own implementations.
KeywordsNatural Language Processing,NLU+,Machine Translation
Contacts
Course organiserDr Adam Lopez
Tel: (0131 6)50 4430
Email: alopez@inf.ed.ac.uk
Course secretaryMr Gregor Hall
Tel: (0131 6)50 5194
Email: gregor.hall@ed.ac.uk
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