Transcript Natural Language Generation: Discourse Planning Paul Gallagher 28 April 2005 (Material adapted from Ch.

Natural Language Generation:
Discourse Planning
Paul Gallagher
28 April 2005
(Material adapted from Ch. 20 of Jurafsky and Martin unless otherwise noted)
Introduction
Programs that generate natural language
are very common


“Hello, world” (Kernighan and Ritchie)
Template filling and mail merge
Simple, but inflexible!
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)
Introduction
Contrast with NLU


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
Introduction
What kinds of choices?



Content selection
Lexical selection
Sentence structure
Aggregation
Referring expressions

Discourse structure
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)

An NLG
Reference
Architecture
Knowledge Base +
Communicative Goal
Meaning
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”
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)
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
Text Schemata
Knowledge base representation of a saving procedure
(Jurafsky and Martin. Fig. 20.5)
Text Schemata
A schema from representing procedures. Implemented as an
augmented transition network (ATN).
Jurafsky and Martin. Fig 20.6
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.
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
Rhetorical Relations
I love to collect classic
automobiles.
nucleus
Elaboration
My favorite car is my 1899
Duryea.
However, I prefer to drive
my 1999 Toyota.
satellite
Contrast
satellite
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.
Rhetorical Relations
Name: Expand Purpose
Effect:
(COMPETENT hearer (DOACTION ?action))
Constraints:
(AND
(c-get-all-substeps ?action
?sub-actions)
(NOT singular-list? ?subactions))
Nucleus:
(COMPETENT hearer (DOSEQUENCE ?sub-actions))
Satellites:
(((RST-PURPOSE (INFORM s
hearer (DO ?action))) *required*))
Name: Expand Sub-Actions
Effect:
(COMPETENT hearer (DOSEQUENCE ?actions))
Constraints:
NIL
Nucleus:
(foreach ?actions (RSTSEQUENCE (COMPETENT
hearer (DO-ACTION ?actions))))
Satellites:
NIL
Jurafsky and Martin, pp. 786 and 788
Rhetorical Relations
The full rhetorical structure for the example text.
Jurafsky and Martin. Fig. 20.7.
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.)