1. Natural Language Generation: Discourse Planning
Paul Gallagher
28 April 2005
(Material adapted from Ch. 20 of Jurafsky and Martin unless otherwise noted)

2. Introduction Programs that generate natural language are very common
“Hello, world” (Kernighan and Ritchie)
Template filling and mail merge
Simple, but inflexible!

3. Introduction
What do we really mean when we say “Natural Language Generation”?
“the process of constructing natural language outputs from non-linguistic inputs” (Jurafsky and Martin, p. 765)
Map from meaning to text (the inverse of natural language understanding)

4. Introduction Contrast with NLU Characteristics of an NLG system
In NLU, focus is hypothesis management
In NLG, focus is choice
Characteristics of an NLG system
Produce an appropriate range of forms
Choose among forms based on internal meaning and context

5. Introduction What kinds of choices? Content selection
Lexical selection
Sentence structure
Aggregation
Referring expressions
Discourse structure

6. Introduction NLG Examples
Generate textual weather forecasts from weather maps
Summarize statistical data from database or spreadsheet
Explain medical information
Authoring aids
(Reiter and Dale)

7. Linguistic Realization
Knowledge Base + Communicative Goal
Meaning
An NLG Reference Architecture
Discourse Planner
Text Plan
Map
Sentence Planning
Sentence Plans
Linguistic Realization
Natural Language Text
Text
Adapted from Dale & Reiter. “Building Applied Natural Language Generation Systems”

8. Discourse Planner
“Discourse planning…[imposes] ordering and structure over the set of messages to be conveyed.” (Reiter and Dale)
Push or Pull? The planner selects or receives its content from the knowledge base. (McDonald)
Outputs a tree structure defining order and rhetorical structure. (Reiter and Dale)

9. Text Schemata
Observation: Many texts follows consistent structural patterns
Example: Instructions
For each step:
Mention preconditions
Describe the step
Describe sub-steps
Mention side-effects

10. Text Schemata Knowledge base representation of a saving procedure
(Jurafsky and Martin. Fig. 20.5)

11. Text Schemata
A schema from representing procedures. Implemented as an augmented transition network (ATN).
Jurafsky and Martin. Fig 20.6

12. Text Schemata
Sample output of the example: Save the document: First, choose the save option from the file menu. This causes the system to display the Save-As dialog box. Next choose the destination folder and type the filename. Finally, press the save button. This causes the system to save the document.

13. Rhetorical Relations Text schemata still not very flexible
Schema is essentially a hard-coded text plan.
There is an underlying structure to language which we can take advantage of to develop richer expressions: Rhetorical Structure Theory

14. Rhetorical Relations I love to collect classic automobiles.
My favorite car is my 1899
Duryea.
However, I prefer to drive
my 1999 Toyota.
nucleus
Elaboration
satellite
Contrast
satellite

15. Rhetorical Relations How do we apply RST to a discourse planner?
Post a high-level goal to the planner (e.g., “Make the hearer competent to save a document”)
Create plan operators which expand goals into sub-goals, creating a rhetorical structure tree.

16. Rhetorical Relations Name: Expand Purpose Effect:
(COMPETENT hearer (DO-ACTION ?action))
Constraints:
(AND
(c-get-all-substeps ?action ?sub-actions)
(NOT singular-list? ?sub-actions))
Nucleus:
(COMPETENT hearer (DO-SEQUENCE ?sub-actions))
Satellites:
(((RST-PURPOSE (INFORM s hearer (DO ?action))) *required*))
Name: Expand Sub-Actions
Effect:
(COMPETENT hearer (DO-SEQUENCE ?actions))
Constraints:
NIL
Nucleus:
(foreach ?actions (RST-SEQUENCE (COMPETENT hearer (DO-ACTION ?actions))))
Satellites:
Jurafsky and Martin, pp. 786 and 788

17. Rhetorical Relations
The full rhetorical structure for the example text.
Jurafsky and Martin. Fig

18. References
Jurafsky, D. & Martin, J. H. (2000). Speech and and Language Processing.
Reiter, E. and Dale, R. (1997). “Building Applied Natural Language Systems.
McDonald, D. D. “Natural Language Generation”. (Appeared in Handbook of Natural Language Generation, edited by Dale, R., Moisl, H., and Somers, H.)