The Third Workshop on Computational Models of Narrative Workshop ...

The Third Workshop on Computational Models of Narrative (CMN’12)

Workshop Programme Saturday, 26 May 2012 12:30 Welcome and Introduction, M.A. Finlayson 13:00 Invited Keynote: Crowd Sourcing Narrative Logic: Towards a Computational Narratology ´ J.C. Meister with CLEA, Session I: Representation 14:00 14:20 14:35 14:50 15:00 15:15 15:30

15:50

Toward Sequencing “Narrative DNA”: Tale Types, Motif Strings and Memetic Pathways, S. Darányi, P. Wittek, L. Forró Computational Models of Narratives as Structured Associations of Formalized Elementary Events, G.P. Zarri Objectivity and Reproducibility of Proppian Narrative Annotations, R. Bod, B. Fisseni, A. Kurji, B. Löwe An Experiment to Determine whether Clustering will Reveal Mythemes, R. Lang, J.G. Mersch In Search of an Appropriate Abstraction Level for Motif Annotations, F. Karsdorp, P. van Kranenburg, T. Meder, D. Trieschnigg, A. van den Bosch Understanding Objects in Online Museum Collections by Means of Narratives, C. van den Akker, M. van Erp, L. Aroyo, R. Segers, L. van der Meij, S. Lêgene and G. Schreiber Best Student Paper on a Cognitive Science Topic: Indexter: A Computational Model of the Event-Indexing Situation Model for Characterizing Narratives, R.E. Cardona-Rivera, B.A. Cassell, S.G. Ware, R.M. Young Coffee Break

16:30

Towards Finding the Fundamental Unit of Narrative: A Proposal for the Narreme, A. Baikadi, R.E. Cardona-Rivera 16:40 People, Places and Emotions: Visually Representing Historical Context in Oral Testimonies, A.T. Chen, A. Yoon, R. Shaw Session II: Corpora 16:55 17:15 17:30 17:50 18:00 18:15 18:35

TrollFinder: Geo-Semantic Exploration of a Very Large Corpus of Danish Folklore, P.M. Broadwell, T.R. Tangherlini A Hybrid Model and Memory Based Story Classifier, B. Ceran, R. Karad, S. Corman, H. Davulcu A Crowd-Sourced Collection of Narratives for Studying Conflict, R. Swanson and A. Jhala Towards a Culturally-Rich Shared Narrative Corpus: Suggestions for the Inclusion of Culturally Diverse Narrative Genres, V. Romero, J. Niehaus, P. Weyhrauch, J. Pfautz, S.N. Rielly Towards a Digital Resource for African Folktales, D.O. Ninan and O.A. Odejobi Formal Models of Western Films for Interactive Narrative Technologies, B. Magerko, B. O’Neill End i

Workshop Programme (cont. . . )

Sunday, 26 May 2012 Session III: Similarity 9:00 9:20 9:35 9:55

Detecting Story Analogies from Annotations of Time, Action and Agency, D.K. Elson Story Comparison via Simultaneous Matching and Alignment, M.P. Fay Similarity of Narratives, L. Michael Which Dimensions of Narratives are Relevant for Human Judgments of Story Equivalence?, B. Fisseni, B. Löwe 10:10 Story Retrieval and Comparison using Concept Patterns, C.E. Krakauer, P.H. Winston

10:30

Coffee Break Session IV: Generation

11:00 11:20 11:40 12:00 12:10 12:30

From the Fleece of Fact to Narrative Yarns: A Computational Model of Composition, P. Gervás Is this a DAG that I see before me?: An Onomasiological Approach to Narrative Analysis and Generation, M. Levison, G. Lessard Automatically Learning to Tell Stories about Social Situations from the Crowd, B. Li, S. LeeUrban, D.S. Appling, M.O. Riedl Prototyping the Use of Plot Curves to Guide Story Generation, C. León, P. Gervás Simulating Plot: Towards a Generative Model of Narrative Structure, G.A. Sack Lunch Session V: Persuasion

14:30 A Choice-Based Model of Character Personality in Narrative, J.C. Bahamon, R.M. Young 14:45 Persuasive Precedents, F. Bex, T. Bench-Capon, B. Verheij 15:00 Integrating Argumentation, Narrative and Probability in Legal Evidence, B. Verheij 15:10 Arguments as Narratives, A. Wyner 15:20 Towards a Computational Model of Narrative Persuasion: A Broad Perspective, J. Niehaus, V. Romero, J. Pfautz, S.N. Reilly, R. Gerrig, P. Weyhrauch 15:30 Discussion 16:00

Coffee Break

16:30 18:00

Discussion End

ii

Editor Mark A. Finlayson

Massachusetts Institute of Technology, USA

Organizing Committee Mark A. Finlayson (Chair) Pablo Gervás Deniz Yuret Floris Bex

Massachusetts Institute of Technology, USA Universidad Complutense de Madrid, Spain Koç University, Turkey University of Dundee, UK

Programme Committee Steve Corman Barbara Dancygier Hasan Davulcu David K. Elson Matthew P. Fay Andrew Gordon Benedikt Löwe Livia Polanyi Emmett Tomai Bart Verheij Patrick H. Winston R. Michael Young

Arizona State University, USA University of British Columbia, Canada Arizona State University, USA Google, USA Massachusetts Institute of Technology, USA Institute for Creative Technologies, USA University of Amsterdam, the Netherlands LDM Associates, USA University of Texas-Pan American, USA University of Groningen, the Netherlands Massachusetts Institute of Technology, USA North Carolina State University, USA

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Table of Contents Invited Keynote 1

´ by Jan Crowd Sourcing Narrative Logic: Towards a Computational Narratology with CLEA Christoph Meister . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

Session I: Representation 2

Toward Sequencing “Narrative DNA”: Tale Types, Motif Strings and Memetic Pathways by Sándor Darányi, Peter Wittek and László Forró . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Computational Models of Narratives as Structured Associations of Formalized Elementary Events by Gian Piero Zarri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Objectivity and Reproducibility of Proppian Narrative Annotations by Rens Bod, Bernhard Fisseni, Aadil Kurji and Benedikt Löwe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 An Experiment to Determine Whether Clustering Will Reveal Mythemes by R. Raymond Lang and John G. Mersch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 In Search of an Appropriate Abstraction Level for Motif Annotations by Folgert Karsdorp, Peter van Kranenburg, Theo Meder, Dolf Trieschnigg and Antal van den Bosch . . . . . . . . . . . . . 7 Understanding Objects in Online Museum Collections by Means of Narratives by Chiel van den Akker, Marieke van Erp, Lora Aroyo, Roxane Segers, Lourens van der Meij, Susan Lêgene and Guus Schreiber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Indexter: A Computational Model of the Event-Indexing Situation Model for Characterizing Narratives by Rogelio E. Cardona-Rivera, Bradley A. Cassell, Stephen G. Ware and R. Michael Young . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Towards Finding the Fundamental Unit of Narrative: A Proposal for the Narreme by Alok Baikadi and Rogelio E. Cardona-Rivera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 People, Places and Emotions: Visually Representing Historical Context in Oral Testimonies by Annie T. Chen, Ayoung Yoon, Ryan Shaw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 11 17 22 24 29 34 44 47

Session II: Corpora 11 TrollFinder: Geo-Semantic Exploration of a Very Large Corpus of Danish Folklore by Peter M. Broadwell and Timothy R. Tangherlini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 A Hybrid Model and Memory Based Story Classifier by Betul Ceran, Ravi Karad, Steven Corman and Hasan Davulcu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 A Crowd-Sourced Collection of Narratives for Studying Conflict by Reid Swanson and Arnav Jhala . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Towards a Culturally-Rich Shared Narrative Corpus: Suggestions for the Inclusion of Culturally Diverse Narrative Genres by Victoria Romero, James Niehaus, Peter Weyhrauch, Jonathan Pfautz and Scott Neal Reilly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Towards a Digital Resource for African Folktales by Deborah O. Ninan and Odetunji A. O . dé.jo.b´ı . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Formal Models of Western Films for Interactive Narrative Technologies by Brian Magerko and Brian O’Neill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

52 60 65 74 77 83

Session III: Similarity 17 18 19 20

Detecting Story Analogies from Annotations of Time, Action and Agency by David K. Elson 91 Story Comparison via Simultaneous Matching and Alignment by Matthew P. Fay . . . . . . . . . . . 100 Similarity of Narratives by Loizos Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Which Dimensions of Narratives are Relevant for Human Judgments of Story Equivalence? by Bernhard Fisseni and Benedikt Löwe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 iv

21 Story Retrieval and Comparison using Concept Patterns by Caryn E. Krakauer and Patrick H. Winston . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Session IV: Generation 22 From the Fleece of Fact to Narrative Yarns: A Computational Model of Composition by Pablo Gervás . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 23 “Is this a DAG that I see before me?” An Onomasiological Approach to Narrative Analysis and Generation by Michael Levison and Greg Lessard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 24 Automatically Learning to Tell Stories about Social Situations from the Crowd by Boyang Li, Stephen Lee-Urban, Darren Scott Appling, and Mark O. Riedl . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 25 Prototyping the Use of Plot Curves to Guide Story Generation by Carlos León and Pablo Gervás . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 26 Simulating Plot: Towards a Generative Model of Narrative Structure by Graham Alexander Sack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Session V: Persuasion 27 A Choice-Based Model of Character Personality in Narrative by Julio César Bahamón and R. Michael Young . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 28 Persuasive Precedents by Floris Bex, Trevor Bench-Capon and Bart Verheij . . . . . . . . . . . . . . . . 171 29 Integrating Argumentation, Narrative and Probability in Legal Evidence by Bart Verheij . . . . . 176 30 Arguments as Narratives by Adam Wyner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 31 Towards a Computational Model of Narrative Persuasion: A Broad Perspective by James Niehaus, Victoria Romero, Jonathan Pfautz, Scott Neal Reilly, Richard Gerrig and Peter Weyhrauch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

v

Author Index Appling, Darren Scott, 142 Aroyo, Lora, 29

Niehaus, James, 74, 181 Ninan, Debrorah O., 77

Bahamón, Julio César, 166 Baikadi, Alok, 44 Bench-Capon, Trevor, 171 Bex, Floris, 171 Bod, Rens, 17 Broadwell, Peter M., 52

O’Neill, Brian, 83 O.dé.jo.b´ı, Odetunji A., 77 Pfautz, Jonathan, 74, 181 Reilly, Scott Neal, 74, 181 Riedl, Mark O., 142 Romero, Victoria, 74, 181

Cardona-Rivera, Rogelio E., 34, 44 Cassell, Bradley A., 34 Ceran, Betul, 60 Chen, Annie T., 47 Corman, Steven, 60

Sack, Graham Alexander, 157 Schreiber, Guus, 29 Segers, Roxane, 29 Shaw, Ryan, 47 Swanson, Reid, 65

Darányi, Sándor, 2 Davulcu, Hasan, 60

Tangherlini, Timothy R., 52 Trieschnigg, Dolf, 24

Elson, David K., 91

van den Akker, Chiel, 29 van den Bosch, Antal, 24 van der Meij, Lourens, 29 van Erp, Marieke, 29 van Kranenburg, Peter, 24 Verheij, Bart, 171, 176

Fay, Matthew P., 100 Fisseni, Bernhard, 17, 114 Forró, László, 2 Gerrig, Richard, 181 Gervás, Pablo, 125, 152 Jhala, Arnav, 65

Ware, Stephen G., 34 Weyhrauch, Peter, 74, 181 Winston, Patrick H., 119 Wittek, Peter, 2 Wyner, Adam, 178

Karad, Ravi, 60 Karsdorp, Folgert, 24 Krakauer, Caryn E., 119 Kurji, Aadil, 17

Yoon, Ayoung, 47 Young, R. Michael, 34, 166

Löwe, Benedikt, 17, 114 Lêgene, Susan, 29 Lang, R. Raymond, 22 León, Carlos, 152 Lee-Urban, Stephen, 142 Lessard, Greg, 134 Levison, Michael, 134 Li, Boyang, 142

Zarri, Gian Piero, 11

Magerko, Brian, 83 Meder, Theo, 24 Meister, Jan Christoph, 1 Mersch, John G., 22 Michael, Loizos, 105 vi

Preface This workshop is third in a series dedicated to advancing a nascent field: the computationallygrounded science of narrative. The past decade has seen a resurgence of interest in a formal understanding of the phenomenon of narrative. Since 1999 there have been over fifteen conferences, symposia, and workshops (including those in this series) focusing on applying computational and experimental techniques to narrative. The field is strongly interdisciplinary: it has engaged researchers across the humanities, social sciences, cognitive sciences, and computer sciences. With this building momentum the coming years promise great advances in our understanding of the fundamental nature of narrative and its place in human cognition and society. We call this workshop series Computational Models of Narrative because we believe that a true science of narrative must adhere to the principle espoused by Herbert Simon in his book The Sciences of the Artificial: that without computational modeling, the science of a complex human phenomenon such as narrative will never be successful. To our mind this expands the workshop’s purview beyond the limited body of effort that directly incorporates computer simulation. It gives us a broad mandate to include a great deal of cognitive, linguistic, neurobiological, social scientific, and literary work: indeed, any research where the researchers have successfully applied their field’s unique insights to narrative in a way that is compatible with a computational frame of mind. We seek work whose results are thought out carefully enough, and specified precisely enough, that they could eventually inform computational modeling of narrative. In keeping with interdisciplinary nature of the field, we have made an explicit decision to move around between different communities so as to enhance engagement, cross-pollination, and visibility (at least for the first handful of workshops). Last year we were hosted by the Association for the Advancement of Artificial Intelligence (AAAI). This year we are hosted by the Language Resources and Evaluation Conference (LREC), which is solidly placed in the computational linguistics community. For the next meeting we will likely set our sights on a conference in cognitive science or neuroscience; after that, probably the humanities or the social sciences. In recognition of a critical blocker, the special focus of this edition of the workshop is the identification, collection, and construction of shared resources and corpora that support the computational modeling of narrative. LREC, therefore, was a perfect venue. This year we are pleased to be giving a best paper award: Best student paper on a cognitive science topic. The award goes to Mr. Rogelio E. Cardona-Rivera, who is a Ph.D. student in computer science at North Carolina State University, for his paper titled “Indexter: A Computational Model of the EventIndexing Situation Model for Characterizing Narratives,” co-authored with Bradley A. Cassell, Stephen G. Ware, and R. Michael Young. This paper award is part of our effort to reach out and engage the diverse communities that are relevant to the computational science of narrative. We thank our sponsors for their support of the workshop. Their continued interest and generosity allowed us to offer a number of travel grants this year, as well as bring in our invited speaker. They include: Robb Wilcox at the Office of Naval Research Global; Ivy Estabrooke of the Human Social Cultural and Behavioral Sciences Program at the Office of Naval Research; and Bill Casebeer at the Defense Advanced Research Projects Agency. The Cognitive Science Society provided support to offer the best student paper award, in the form of a check and complimentary membership in the society for the next year. Mark A. Finlayson Workshop Chair Cambridge, Massachusetts vii

Invited Keynote: Crowd Sourcing Narrative Logic: Towards a Computational Narratology with ´ CLEA Jan Christoph Meister Department of Language, Literature and Media I, Faculty of Humanities Universität Hamburg, Hamburg, Germany [email protected] Abstract I discuss a collaborative, computer aided approach towards building and exploiting a shared resource that can aid further research into the history and development of narrative, as well as into its phenomenology and logic. The particular example illustrating this approach ´ short for Collaborative Literature Exploration ´ is a project called CLEA, and Annotation.

´ CLEA, a Google Digital Humanities Award funded project1 , is a browser based annotation and text analysis environment which comprises three functional modules:

and not real-world entities that can be found ”out there” and on their own. ´ amounts to more than merely facilitating However, CLEA a new praxis of analysing and modeling the symbolic processes and products that human cultures designate as ‘narrative’. In a methodological perspective and with regard to the current transformation of the humanities in general, ´ also demonstrates the impact and potential relevance CLEA of the new scientific paradigm of the Digital Humanities. In a disciplinary perspective on the other hand, and in particular in that of narratology and of computational science, CLA may be considered as an example for an emerging inter-discipline, tentatively labeled by Inderjeet Mani and others as Computational Narratology.2 Accordingly, I will ´ as a conpay equal attention to the demonstration of CLEA crete example, and to the reflexion of broader methodological and programmatic consequences. Adopting a computer aided, crowd sourcing based approach in the study and modelling of narrative, I believe, will do more than afford us new insights into the logic of narratives as objects of our research. It will also help us to understand better on which premises our reasoning about narrative is based, and which contingencies and constraints — disciplinary as well as cultural — we might have to take into account in our future work.

1. a working environment for highly unconstrained, nondeterministic collaborative markup of narratives; 2. a repository which manages and distributes object -)*62%&'>"?1227"

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Figure 1: Example SIG encoding for a non-contiguous fragment of “The Wily Lion”. nodes contain text spans and are connected to three P nodes with equivalent propositions. There are two interpreted goals in this encoding: The bull has a goal to eat grass (his will to live is implied), and the lion has a goal to eat the bull. Note that frames themselves refer to mental states: The first sentence implies that the bull has a desire to eat grass, and directly asserts that it is, in fact, eating grass, such that he begins the story with a satisfied goal; later, the “goal content” of eating grass is ceased when the lion eats the bull. This event coreference—the same action is desired, achieved and then lost—forms the basis of the SIG approach to modeling narrative cohesion. A plan is modeled as a chain of connected nodes inside a Goal frame. Each node is a “subgoal” that leads to the ultimate goal at the end of the chain. The connections are directed arcs that indicate causality, as expected by the agent associated with the frame. Specifically, a would cause (wc) relation traverses from one interpretative frame or proposition to another interpretative frame or proposition. It signifies that in the belief context of the originating node, an actualization of the originating node would causally lead to (is both necessary and sufficient for) an actualization of the destination node. Would prevent (wp) is its complement, signifying a belief that the actualization of the originating node would cause the destination node to be prevented/ceased. Two other relations, precondition for (pf) and precondition against (pa), signify a belief that actualization of the originating node is necessary, but not sufficient, for the actualization or prevention/cessation (respectively) of the destination node. This schematic bears resemblance to a partial-order plan, the key difference being that a SIG plan is an annotator’s interpretation of a narrated agent’s intentions (Suh and Trabasso, 1993), rather than a solvable system (Riedl and Young, 2004). Finally, the affectual impact of a P node or actualized I node can be indicated through the combination of an Affect (A) node (which indicates a particular agent) and a provides for (p) or a damages (d) arc (which indicates positive or negative impact, respectively). For instance, in Figure 1

93

the bull’s eating of grass is intrinsically good for the bull, while the lion’s eating of the bull is good for the lion but bad for the bull. In a properly formed SIG encoding, every goal is annotated with its affectual impact either through a direct arc or through a path through a plan. The bull’s removing its horns, for instance, is indirectly good for the lion because it satisfies part of the lion’s plan. This set of nodes and arcs forms a basic vocabulary and syntax from which complex narrative structures can be constructed. This can be seen through the enumeration of “SIG patterns”—compound relations serve as fragments of abstract narratives. We can define a set of a priori SIG patterns to represent a range of narrative scenarios, in a manner similar to Lehnert’s enumeration of plot units but with a greater emphasis on temporal and agentive (theoryof-mind) relationships. Notably, these patterns are defined only in terms of node and arc permutations, without any notion of particular propositional content within P and I nodes (except to identify the agent, such as P:X for agent X). We have identified 80 patterns, fourteen of which are shown in Figure 2, in several categories: affectual status transitions (e.g., gain, loss, mixed blessing), single-agent goals (problem, obstacle), outcomes (backfire, lost opportunity, recovery, peripeteia), beliefs (surprise, anagroisis, false dawn), dilemmas, two-agent interactions (selfless act, conflict, coercion, betrayal), persuasion and deception (mutual deception), time (flashback, suspense), mystery, and contradictory points of view. The dotted arc labeled act represents any of the arcs that actualize (ia, i or ac). Though an incomplete list of possible, thematically relevant patterns, they outline the range of what can be expressed by permuting these relations. The DramaBank collection project, underway and publicly available,2 elicits SIG encodings for stories in various genres from trained annotators. Each machine-readable record includes a reproduction of the source text as well as the nodes and relations of the annotator’s encoding (serialized as first-order predicates). The collection includes 2

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