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Automatically Generating Fictional and Factual Narratives
Birte Lönneker-Rodman
Écritures de l’histoire, écritures de la fictionColloque EHESS-CNRS, Paris, FranceMarch 16, 2006
Lönneker-Rodman: Automatically Generating Narratives 2March 16, 2006
Overview
Automatically Generated Narratives Models of Narrative and Generation Structure and Acquisition of Knowledge Author and Narrator in Generation Fiction-Specific Language Use Conclusion
Lönneker-Rodman: Automatically Generating Narratives 3March 16, 2006
Automatically Generated Narratives
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Example I: Tale-Spin output, abbreviated
Once upon a time George Ant lived near a patch of ground. There was a nest in an ash tree. Wilma Bird lived in the nest. […] George was very thirsty. George wanted to get near some water. George walked from his patch of ground across the meadow […] to a river bank. George fell into the water. […] George wanted to get near the meadow. George couldn‘t get near the meadow…
Lönneker-Rodman: Automatically Generating Narratives 5March 16, 2006
Example I, continued
… Wilma wanted to get near George. Wilma grabbed George with her claw. Wilma took George from the river […] to the meadow. George was devoted to Wilma. George owed everything to Wilma. Wilma let go of George. George fell to the meadow. The end. (Meehan 1979; Meehan 1981)
Lönneker-Rodman: Automatically Generating Narratives 6March 16, 2006
Example II: Newspaper reports
Today in Paris, a policeman was killed and four others wounded by anarchists who exploded a remote controlled bomb under the truck in which they were going from their office to a restaurant. The bomb contained two kilos of dynamite.
Anarchists killed a policemen and wounded four others today in Paris. They exploded a remote controlled bomb under the truck in which they were going […]. The bomb contained […]. (Danlos 1987)
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Classification Criteria for Generators
Main purpose Utilitarian (money) Cognitive (theory) Experimental (fun, discovery)
Genre/text type Fictional (Story Generation) Factual (non-fictional) (Natural Language Generation)
Interactivity Narrative theory "Level" (granularity) of generated output
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Models of Narrative and Generation
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Occurrences(Geschehen)
Story(Geschichte)
Narrative(Erzählung)
Presentation of Narrative(Präsentation der Erzählung)
(Schmid 1982; 2005)
Two-Level and Four-Level Models of Narrative
Histoire
Discours
Occurrences(Geschehen)
Selection
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Two-Level and Four-Level Models of Natural Language Generation
Deep Generation
Surface Generation
(McKeown/Swartout 1988)
Document Structuring
Microplanning
Surface Realization
(Reiter/Dale 2000, adapted)
Content Determination
Doc
umen
t P
lann
ingContent Determination
Lönneker-Rodman: Automatically Generating Narratives 11March 16, 2006
Structure and Acquisition of Knowledge
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Knowledge Base and Memory
Generators need some knowledge (world knowledge, data, information, …) to start with (static representation) Just read this data out (not really „Generation“) Instantiate, combine, and transform this knowledge;
keep track of changes in narrated world (Memory; dynamic representation)
Receive information on changes in the world from "host application"
meteorological service hand-held Digital Assistant [Callaway et al. 2005] battle simulator (Maybury 1999)
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Knowledge Representation in Graph
Kidnapping Tormenting at night
Call for help Release of hero Announcement of misfortune
Start of counteraction vs. villain
Fight in an open field Departure of hero
(Maranda 1985)
Kidnapping
Release of hero
Start of counteraction vs. villain
Departure of heroFight in an open field
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Representation of Goals and Plans
Example: Persuade someone by either asking them, giving them food or threatening them
(DE PERSUADE (ACTOR AGENT ACTION RESULT) (GOAL-EVAL ACTOR ACTION) (APPEND (LIST ASK-PLAN)
(GEN-PLANS 'FOOD (GET-ISA 'FOOD AGENT) BARGAIN-
PLAN)(LIST THREAT-PLAN]
(Meehan 1981:237)
Lönneker-Rodman: Automatically Generating Narratives 15March 16, 2006
Messages from "Host Application"
Battle event messages: timestamp, message, sender
(2774438460 (OCA 100 BEGIN MISSION EXECUTION) WOC-50TACTICAL-FIGHTER-WING)(2774438460 (50-TACTICAL-FIGHTER-WING DISPENSE 4 F-16AIRCRAFT) OCA100)(2774439140 (MOBILE-SAM2 FIRE A MISSILE AT OCA100) CLOCK)(2774439140 (MOBILE-SAM1 FIRE A MISSILE AT OCA100) CLOCK)[…]
(Maybury 1999)
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Filling the Knowledge Base
Types (classes) of objects and events and relations between them: Ontology Manual encoding Semi-automated acquisition from texts (Web)
Tokens (instances) of objects and events Manual input Data from host application Data from existing texts (Information Extraction) Dynamic generation in planning
FI, NONFI
FI, NONFI
NONFI
NONFI
FI
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Author and Narrator in Generation
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Authorship: Three main viewpoints
Generation shows that there is no author Balpe 1995
Authorship distributed Knowledge encoder Programmer of process algorithms Interactor (e.g. provider of individual data, post-editor)
Lenoble 1995 "Computer as author"
if no human intervention required when creating texts otherwise: "authoring aid"
Reiter and Dale 2000
FI
FI
NONFI
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Narrator in Generation
Heterodiegetic "Today in Paris, a policeman was killed and four others wounded
by anarchists […]" (Danlos) "George wanted to […] George was devoted to […]" (Tale-Spin)
Heterodiegetic, overt "It pisses me off that a few shiftless students were out to make
trouble on Beinecke plaza one day: they built a shanty town, Winny Mandela city, because they wanted […]" (PAULINE, Hovy 1988)
You-narrative " […] It seems that you were mainly interested in the winter
activities […] since you spent most of the time in front of the January and February frescos […]" (Callaway et al. 2005)
FI
NONFI
NONFI
NONFI
Narrator function always filled by
generator?
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Fiction-Specific Language Usein Generation
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Narrative Prose Generation
Character-to-character dialogue Typographic marks Speaker references himself, hearer, others, … Interjections
Embedding the dialogue Reference communicative action utterance manner, …
Discourse markers Now, it happened that […]. So, after following her
[…], whereupon he said, "Good day, Little Red Riding Hood, […]".
(Callaway and Lester 2002)
FI, NONFI
FI, NONFI
FI NONFI
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Conclusion
Generation confirms fictional vs. non-fictional (factual) distinction, but brings us back to the same old questions
Automatically generated factual narratives: reference to real world as precise as measuring instruments (observations) No "perceptual" facet of perspective, but still: Selection and inferences rely on world knowledge of generator
What about … … Maybury's simulation? … Inputting "fake" facts into Danlos's news report generator? … Inputting "real" actors into Meehan's Tale-Spin?