We present the Narrator, an NLG component used for the generation of narratives in a digital storytelling system. We describe how the Narrator works and show some examples of generated stories

Hielkema, Feikje

Slabbers, Nanda

Theune, Mariët

English

Theune, Mariet

University of Twente Research Information

The Narrator: NLG for digital storytellingMariët TheuneHuman Media InteractionUniversity of TwenteEnschede, The Netherlandsm.theune@utwente.nlNanda SlabbersHuman Media InteractionUniversity of TwenteEnschede, The Netherlandsnandaslabbers@hotmail.comFeikje Hielkema∗Department of Computing ScienceUniversity of AberdeenScotland, UKfhielkem@csd.abdn.ac.ukAbstractWe present the Narrator, an NLG component usedfor the generation of narratives in a digital story-telling system. We describe how the Narrator worksand show some examples of generated stories.1 IntroductionThe automatic generation of narratives is still alargely unexplored field in NLG. Some exceptionsare STORYBOOK (Callaway, 2000), a narrativeprose generation system that can generate many dif-ferent retellings of the same story (Little Red Rid-ing Hood) and the architecture for a “narratologi-cally enhanced NLG system” proposed by Lönneker(2005). An NLG system that is actually used in adigital storytelling application is PRINCE (Herváset al., 2006).Here we present the Narrator: the NLG compo-nent of the Virtual Storyteller, a multi-agent systemthat automatically creates fairy tales based on theactions of autonomous character agents in a sim-ulated story world, where they can perform goal-oriented actions and experience emotions (Theuneet al., 2004). The emerging story is captured in aformal representation and fed to the Narrator, whichexpresses it in natural language (in our case, Dutch).In the rest of this paper, we give a brief overviewof the subsequent tasks the Narrator carries out togenerate a fluent, well-formed narrative. We focuson the generation of referring expressions; a moredetailed description of the entire generation processcan be found in Theune et al. (2007). For more in-formation concerning various design decisions seeTheune et al. (2006).∗ Feikje Hielkema carried out this work while she was at theUniversity of Groningen, The Netherlands.2 Document planningThe input for the Narrator is a Fabula (Swartjes andTheune, 2006): a story representation in the formof a causal network linking the following plot ele-ments: actions, events, perceptions, goals, goal out-comes, and characters’ “internal elements” such asemotions and beliefs. Possible links between theseelements are motivation, enablement, mental andphysical cause relations. Also, each plot element isassociated with a time stamp (in terms of time stepsin the story world). The (simplified) example Fab-ula in Figure 1 represents a very short story abouta dwarf who is hungry and believes there is an ap-ple in the house, leading to the goal to eat the apple.To achieve this, the dwarf carries out a simple plan:taking the apple and then eating it, which leads tothe perception and the belief that the apple has beeneaten, signifying a positive goal outcome.As a first step in turning a Fabula into a DocumentPlan, all information that is not relevant for narra-tion must be pruned away. An example is the stan-dard perception-belief-positive outcome chain thatfollows a successful action: for the narrative, it issufficient to mention only that the action was carriedout. Currently, this process is not yet implementedin the Narrator; however, we assume that in the ex-ample Fabula, the nodes following the ‘Eat Apple’action will be pruned away. The next step is to con-vert the pruned Fabula to a binary tree, replacing thecausal links with appropriate rhetorical relations be-tween plot elements. The basic rhetorical relationsused in the Narrator are Cause, Contrast, Tempo-ral and Additive, with more specific subclasses suchas Purpose and Elaboration. When mapping thelinks in the Fabula to rhetorical relations, consecu-tive steps of a plan are connected using a TemporalFigure 1: Fabula (left) and corresponding Document Plan (right).relation. Motivation and psychological cause rela-tions are mapped to Volitional Cause relations, andenablement and physical cause relations are mappedto Non-volitional Cause relations. Additive is themost general relation. It is used if two plot elementscause another plot element together, and more ingeneral to connect two plot elements that do nothave a more specific relation holding between them.We are currently investigating the automatic deriva-tion of Contrast relations. The final step is to extendthe Document Plan with a setting and backgroundinformation about characters and objects. The ex-ample Document Plan in Figure 1 shows these ex-tensions in grey: a Setting element introducing theprotagonist, and an element specifying the name ofthe protagonist, connected via an Elaboration rela-tion. They are in a Temporal-once (Once upon atime...) relation to the rest of the plot; this relationwas added specifically for the fairy tale domain.3 Sentence planning, lexicalisation andsyntactic aggregationNext, the leaves of the Document Plan are mappedto Dependency Trees. For each type of plot el-ement a template is available telling exactly howits arguments should appear in the correspondingDependency Tree. For example, actions are ex-pressed using a straightforward active voice con-struction, with an optional PP argument to expressinstruments, e.g., The knight opened the gate (witha key).1 For internal states, there are templates forstandard sentences such as The princess was scaredbut also for storytelling-style constructions such asOh, how happy she was! and She had never been sohappy!, to be used for emotions with a high inten-sity. After the Dependency Trees are selected, theirnodes are mapped to Dutch words (except the nodesreferring to entities, which are lexicalised as part of1For reading ease, Narrator output is translated to English.Figure 2: Aggregated Dependency Tree.referring expression generation). The lexical choicealgorithm uses a discourse history, to achieve somevariation in wording by taking into account whichwords have been used recently. The words added tothe Dependency Trees are still uninflected, as mor-phology is taken care of during Surface Realization.The Narrator uses a syntactic aggregation algo-rithm that combines pairs of Dependency Trees andadds an appropriate cue phrase to signal their rhetor-ical relation. The properties of this cue phrase deter-mine which syntactic construction is used to com-bine the Dependency Trees. If the resulting treecontains repeated elements, these can be ellipted.Figure 2 shows an example where the subject of thesecond clause is deleted (Conjunction Reduction).A corresponding surface string could be The dwarfwas hungry and believed there was an apple in thehouse, expressing the Additive relation in the Docu-ment Plan of Figure 1. To keep the aggregated sen-tences from getting too complex, at most three sim-ple Dependency Trees can be combined. In caseswhere this restriction prohibits aggregation, rhetor-ical relations are expressed using adverbs such asthen, however etc. For a more detailed descriptionof the aggregation process, see Theune et al. (2006).4 The generation of referring expressionsTo determine whether a pronoun or a noun shouldbe used to refer to a certain entity, we use a vari-ant of the algorithms of McCoy and Strube (1999)and Henschel et al. (2000). Based on an analysis ofhuman-written fairy tales we determined that pro-nouns are dispreferred (1) at the beginning of a para-graph, (2) if the antecedent has not been mentionedfor two sentences, (3) if a pronoun has been usedfour times and the referring expression is the firstone in the sentence. Also, pronouns cannot be usedwhen the referring expression should express addi-tional information (e.g., about a character’s emo-tional state) in the form of an adjective or a relativeclause. If the above conditions do not hold, a pro-noun is used if there is either strong parallelism or acausal relationship with the previous clause or sen-tence (Chambers and Smyth, 1998; Kehler, 2002);otherwise the decision is based on the salience ofthe referent (Lappin and Leass, 1994).If a noun phrase is to be generated, the referent’sname (if available) is used in 25% of the cases, ei-ther on its own or in a construction such as princessAmalia if the noun describes a function, such asprincess, king or knight. If a description is to begenerated, first a noun has to be selected. Some con-cepts have both a preferred lexicon entry (the mostcommon word for that concept) and one or more ad-ditional entries that are used occasionally for varia-tion. After having selected the noun, different typesof adjectives can be added. Distinguishing adjec-tives, necessary to create an unambiguous referringexpression, are selected only for subsequent refer-ences using a modified version of the algorithm ofKrahmer and Theune (2002). When introducing anew entity all its properties are mentioned, becausethey can be used as distinguishing properties later inthe story. Non-distinguishing adjectives include ad-jectives describing a character’s internal state, and‘decorational’ adjectives are used to spice up the de-scriptions of entities that have no specific propertiesexcept their type (e.g., a heavy gate). Finally, an in-definite article is added when the entity has not beenmentioned before, and a definite article otherwise.Simple inference rules are used to generate bridg-ing descriptions. If the referent (e.g., a gate) is re-lated to a discourse-old entity (e.g., a castle), thealgorithm checks if there is a rule saying “all cas-tles have gates”. If there are no other salient entitiesfor which the same rule holds (i.e., that they alwayshave gates), then a bridging description like the gatecan be used instead of the gate of the castle.5 Surface form generationWhen referring expression generation is finished,the Surface Realiser linearises the now fully lexi-calised Dependency Trees. It traverses them depth-first, ordering the children of each node by gram-mar rules such as SMAIN→ SU + HD + OBJ, whichstates that if a parent node has syntactic category‘SMAIN’ (sentence) and three children with depen-dency labels ‘SU’ (subject), ‘OBJ’ (direct object)and ‘HD’ (main verb), then those children shouldbe ordered in the above way. This particular rulewould for instance be applied to produce the sen-tence The prince loved Amalia. Any nouns, adjec-tives and verbs are inflected at the moment they arelinearised. Punctuation is added once linearisationis complete. This concludes our description of theNarrator; for more details see (Theune et al., 2007).6 ExamplesOur simple example story about the hungry dwarfis narrated as follows:Once upon a time there was a dwarf called Plop. Hewas hungry and believed there was an apple in a house.2Therefore he wanted to eat the apple. After Plop hadtaken the apple, he ate it.This story is well-formed and coherent, butalso quite simple. The next story better illustratesthe Narrator’s potential, including examples ofDocument Plan extension, aggregation, pronomi-nalization, and the use of ‘decorational’ adjectives(a high tree). It was generated from a hand-madeDocument Plan (shown in Figure 3), which containsContrast relations and paragraph boundaries thatcannot currently be generated automatically bythe Document Planner. So the story illustratesthe output quality that could be achieved by theNarrator once these remaining Document Planningproblems are resolved:Once upon a time there was a beautiful princess calledAmalia. A knight from a far away country was in lovewith her, but she was in love with a young prince. Theknight was jealous, so he wanted to abduct her.After the knight had climbed a high tree, he jumpedinto the princess’ bedroom. She was so scared that shescreamed loudly, but nobody heard her.The knight grabbed the princess and then he placedher on his horse. After that he took her to an old andnarrow bridge. On the other side she saw the prince shewas in love with. Oh, how relieved princess Amalia was!2Assuming that the house belongs to Plop, the bridging de-scription the house would be more appropriate here. However,in this case the Narrator lacked knowledge about the owner ofthe house, so a general indefinite description was produced.Figure 3: Initial Document Plan for the second example story.7 Concluding remarksThe Narrator has been implemented (in Java), butso far has only been tested with hand-made inputstructures, because parts of the Document Plannerand of the Virtual Storyteller’s plot generation com-ponent are still under construction. So far, the onlyevaluations have been informal comparisons withearlier versions. The Narrator does not employthe kind of narratological knowledge proposed byLönneker (2005), and unlike STORYBOOK it can-not handle narrative aspects such as multiple view-points or character dialogue. However, it can gener-ate well-formed and fluent stories containing sometypical narrative constructions. Currently being in-vestigated are the automatic placement of paragraphboundaries, detection of contrast relations and lex-icalisation of emotions, taking their intensity intoaccount. In addition, as pointed out by one of ourreviewers, it would be beneficial to add a form of se-mantic aggregation to the system, grouping relatedplot elements together during document planning.Our main long-term challenge is to generate textsthat are not only grammatical and coherent, but thatcan also really affect the reader by employing nar-rative techniques such as the use of subjective per-spectives to heighten identification, and foreshad-owing to increase suspense. Ablation tests in thestyle of Callaway (2000) could then be used to eval-uate the effect of such techniques.ReferencesC. Callaway. 2000. Narrative Prose Generation.Ph.D. thesis, North Carolina State University,Raleigh, NC.G.C. Chambers and R. Smyth. 1998. Structuralparallelism and discourse coherence: A test ofCentering Theory. 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McCoy and M. Strube. 1999. Generatinganaphoric expressions: Pronoun or definite de-scription? In Proceedings of the ACL Workshopon The Relation of Discourse/Dialogue Structureand Reference, pages 63–71.I. Swartjes and M. Theune. 2006. A Fabula modelfor emergent narrative. In Technologies for In-teractive Digital Storytelling and Entertainment(TIDSE), LNCS 4326, pages 95–100.M. Theune, S. Rensen, R. op den Akker, D. Heylen,and A. Nijholt. 2004. Emotional characters forautomatic plot creation. In Technologies for In-teractive Digital Storytelling and Entertainment(TIDSE), LNCS 3105, pages 95–100.M. Theune, F. Hielkema, and P. Hendriks. 2006.Performing aggregation and ellipsis using dis-course structures. Research on Language andComputation, 4(4):353–375.M. Theune, N. Slabbers, and F. Hielkema. 2007.The automatic generation of narratives. In Pro-ceedings of CLIN-17. to appear.

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