Abstract. This paper presents a method for learning models of character linguistic style from a corpus of film dialogues and tests the method in a perceptual experiment. We apply our method in the context of SpyFeet, a prototype role playing game. In previous work, we used the PERSONAGE engine to produce restaurant recommendations that varied according to the speaker's personality [14,12]. Here we show for the first time that: (1) our expressive generation engine can operate on content from the story structures of an RPG; (2) PERSONAGE parameter models can be learned from film dialogue; (3) PERSONAGE rule-based models for extraversion and neuroticism are be perceived as intended in a new domain (SpyFeet character utterances); and (4) that the parameter models learned from film dialogue are generally perceived as being similar to the character that the model is based on. This is the first step of our long term goal to create off-theshelf tools to support authors in the creation of interesting dramatic characters and dialogue partners, for a broad range of types of interactive stories and role playing games.

Storytelling serves many different social functions, e.g. stories are used to persuade, share troubles, establish shared values, learn social behaviors, and entertain. Moreover, stories are often told conversationally through dialog, and previous work suggests that information provided dialogically is more engaging than when provided in monolog. In this paper, we present algorithms for converting a deep representation of a story into a dialogic storytelling, that can vary aspects of the telling, including the personality of the storytellers. We conduct several experiments to test whether dialogic storytellings are more engaging, and whether automatically generated variants in linguistic form that correspond to personality differences can be recognized in an extended storytelling dialog

Many forms of interactive digital entertainment involve interacting with virtual dramatic characters. Our long term goal is to procedurally generate character dialogue behavior that automatically mimics, or blends, the style of existing characters. In this paper, we show how linguistic elements in character dialogue can define the style of characters in our RPG SpyFeet. We utilize a corpus of 862 film scripts from the IMSDb website, representing 7,400 characters, 664,000 lines of dialogue and 9,599,000 word tokens. We utilize counts of linguistic reflexes that have been used previously for personality or author recognition to discriminate different character types. With classification experiments, we show that different types of characters can be distinguished at accuracies up to 83% over a baseline of 20%. We discuss the characteristics of the learned models and show how they can be used to mimic particular film characters.

Conversation is a critical component of storytelling, where key information is often revealed by what/how a character says it. We focus on the issue of character voice and build stylistic models with linguistic features related to natural language generation decisions. Using a dialogue corpus of the television series, The Big Bang Theory, we apply content analysis to extract relevant linguistic features to build character-based stylistic models, and we test the model-fit through an user perceptual experiment with Amazon's Mechanical Turk. The results are encouraging in that human subjects tend to perceive the generated utterances as being more similar to the character they are modeled on, than to another random character.

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