Eun Young Ha

Modeling Dialogue Structure with Adjacency Pair Analysis and Hidden Markov Models

Automatically detecting dialogue structure within corpora of human-human dialogue is the subject of increasing attention. In the domain of tutorial dialogue, automatic discovery of dialogue structure is of particular interest because these structures inherently represent tutorial strategies or modes, the study of which is key to the design of intelligent tutoring systems that communicate with learners through natural language. We propose a methodology in which a corpus of human-human tutorial dialogue is first manually annotated with dialogue acts. Dependent adjacency pairs of these acts are then identified through X2 analysis, and hidden Markov modeling is applied to the observed sequences to induce a descriptive model of the dialogue structure.

Archetype-Driven Character Dialogue Generation for Interactive Narrative

Recent years have seen a growing interest in creating virtual agents to populate the cast of characters for interactive narrative. A key challenge posed by interactive characters for narrative environments is devising expressive dialogue generators. To be effective, character dialogue generators must be able to simultaneously take into account multiple sources of information that bear on dialogue, including character attributes, plot development, and communicative goals. Building on the narrative theory of character archetypes, we propose an archetype-driven character dialogue generator that uses a probabilistic unification framework to generate dialogue motivated by character personality and narrative history to achieve communicative goals. The generators behavior is illustrated with character dialogue generation in a narrative-centered learning environment, Crystal Island .

Chapter 12 – Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks

In digital games goal recognition centers on identifying the concrete objectives that a player is attempting identifying the concrete objectives that a player is attempting to achieve given a domain model and a sequence of actions in a virtual environment. Goal-recognition models in open-ended digital games introduce opportunities for adapting gameplay events based on the choices of individual players, as well as interpreting player behaviors during post hoc data mining analyses. However, goal recognition in open-ended games poses significant computational challenges, including inherent uncertainty, exploratory actions, and ill-defined goals. This chapter reports on an investigation of Markov logic networks (MLNs) for recognizing player goals in open-ended digital game environments with exploratory actions. The goal-recognition model was trained on a corpus collected from player interactions with an open-ended game-based learning environment known as C rystal I sland . We present experimental results, in which the goal-recognition model was compared to n -gram models. The findings suggest the proposed goal-recognition model yields significant accuracy gains beyond the n -gram models for predicting player goals in an open-ended digital game.

Inferring Tutorial Dialogue Structure with Hidden Markov Modeling

The field of intelligent tutoring systems has seen many successes in recent years. A significant remaining challenge is the automatic creation of corpus-based tutorial dialogue management models. This paper reports on early work toward this goal. We identify tutorial dialogue modes in an unsupervised fashion using hidden Markov models (HMMs) trained on input sequences of manually-labeled dialogue acts and adjacency pairs. The two best-fit HMMs are presented and compared with respect to the dialogue structure they suggest; we also discuss potential uses of the methodology for future work.