UC Santa Cruz

Conversation is an essential component of social behavior, one of the primary means by which humans express emotions, moods, attitudes, and personality. Conversation is also critical to storytelling, where key information is often revealed by what a character says, how s/he says it, and how s/he reacts to what other characters say.

Interactive narrative systems (INS) are a type of playable media whose applications range from simple entertainment to systems for learning, training, and decision making. Many forms of INS involve interactions with virtual human characters. Thus a key technical capability for such systems is the ability to support natural conversational interaction. While most INS use hand-crafted character dialogue to produce high quality utterances, they suffer from problems of portability and scalability, or what has been called the authoring bottleneck. We believe Natural Language Generation (NLG) is part of the solution to alleviate such burden from authors by automatically generating character dialogue.

Here we focus on the issue of character voice. One way to produce believable, dramatic dialogue is to build stylistic models with linguistic features related to NLG decisions. Film/television dialogue are exemplars of many different linguistic styles that were designed to express dramatic characters. Thus we construct a corpus of film/television character dialogue from screenplays and transcripts publicly available from websites such as the Internet Movie Script Database. We apply content analysis and language modeling techniques to extract relevant linguistic features to build character-based stylistic models. We also apply machine learning techniques to discriminate characters base on available metadata such as genre, year, and director.

This thesis consists of two parts. The first part involves building a basic character model with film dialogue, and then applying the model to an existing expressive NLG engine to generate different character voices. We then evaluate the generation experiment with a perceptual study, which suggests several natural extensions.

The second part involves building a more refined model with television dialogue in order to explore a broader range of stylistic features that can be used to express dramatic characters. We test the model-fit of character models in two ways: 1) ranking experiments to pick out corresponding character’s utterances from a pool of mixed, original characters utterances, and 2) a second generation experiment to test user perceptions of characters.