Last modified: 05 Dec 2025 14:16
The aim of the course is to introduce students who have some background in computing to (1) the varied aims for which Natural Language Generation (NLG) is pursued, (2) the main rule based and statistical methods that are used in NLG, and (3) some of the main NLG algorithms and systems. The course will cover NLG both as a theoretical enterprise (e.g., for constructing models of language production) and as practical language engineering, paying particular attention to the link between NLG and data science. Some programming experience is expected.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Offshore | Sustained Study | No |
| Co-ordinators |
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The course will cover topics including:
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 50 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Word count: 10-15 pages |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Apply | Students will be able to conceive, design and implement commercially attractive NLG products. |
| Procedural | Apply | Students will be able to build NLG applications for given requirements. |
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Word count: 4-6 pages
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Understand | Students will gain a thorough understanding of the statistical and neural NLG techniques. |
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Apply | Students will be able to build NLG applications for given requirements. |
| Procedural | Apply | Students will gain a thorough understanding of the subtasks in the NLG pipeline. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
If a student fails a group element, the resit will be an individual report |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Apply | Students will gain a thorough understanding of the subtasks in the NLG pipeline. |
| Procedural | Understand | Students will gain a thorough understanding of the statistical and neural NLG techniques. |
| Procedural | Apply | Students will be able to build NLG applications for given requirements. |
| Conceptual | Apply | Students will be able to conceive, design and implement commercially attractive NLG products. |
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