automatically generating fictional and factual narratives birte lönneker-rodman Écritures de...

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


Automatically Generated Narratives


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…


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)


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)


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


Models of Narrative and Generation


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


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


Structure and Acquisition of Knowledge


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)


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


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)


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)


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


Author and Narrator in Generation


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


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?


Fiction-Specific Language Usein Generation


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


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?

automatically generating fictional and factual narratives birte lönneker-rodman Écritures de...

Documents

generation slide

meadow slide

acquisition of knowledge

time george ant

generation structure

continued wilma

wilma bird

remote controlled bomb