A longstanding objective of human-computer interaction research is to develop better dialog systems for end users. The subset of user modelling research specifically, aims to provide dialog researchers with models of user behaviour to aid with the design and improvement of dialog systems. Where dialog systems are commercially deployed, they are often to be used by a vast number of users, where sub-optimal performance could lead to an immediate financial loss for the service provider, and even user alienation. Thus, there is a strong incentive to make dialog systems as functional as possible immediately, and crucially prior to their release to the public. Models of user behaviour fill this gap, by simulating the role of human users in the lab, without the losses associated with sub-optimal system performance. User models can also tremendously aid design decisions, by serving as tools for exploratory analysis of real user behaviour, prior to designing dialog software. User modelling is the central problem of this thesis. We focus on a particular kind of dialogs termed task-oriented dialogs (those centred around solving an explicit task) because they represent the frontier of current dialog research and commercial deployment. Users taking part in these dialogs behave according to a set of user goals, which specify what they wish to accomplish from the interaction, and tend to exhibit variability of behaviour given the same set of goals. Our objective is to capture and reproduce (at the semantic utterance level) the range of behaviour that users exhibit while being consistent with their goals. We approach the problem as an instance of generative probabilistic modelling, with explicit user goals, and induced entirely from data. We argue that doing so has numerous practical and theoretical benefits over previous approaches to user modelling which have either lacked a model of user goals, or have been not been driven by real dialog data. A principal problem with user modelling development thus far has been the difficulty in evaluation. We demonstrate how treating user models as probabilistic models alleviates some of these problems through the ability to leverage a whole raft of techniques and insights from machine learning for evaluation. We demonstrate the efficacy of our approach by applying it to two different kinds of task-oriented dialog domains, which exhibit two different sub-problems encountered in real dialog corpora. The first are informational (or slot-filling) domains, specifically those concerning flight and bus route information. In slot-filling domains, user goals take categorical values which allow multiple surface realisations, and are corrupted by speech recognition errors. We address this issue by adopting a topic model representation of user goals which allows us capture both synonymy and phonetic confusability in a unified model. We first evaluate our model intrinsically using held-out probability and perplexity, and demonstrate substantial gains over an alternative string-goal representations, and over a non-goal-directed model. We then show in an extrinsic evaluation that features derived from our model lead to substantial improvements over strong baseline in the task of discriminating between real dialogs (consistent dialogs) and dialogs comprised of real turns sampled from different dialogs (inconsistent dialogs). We then move on to a spatial navigational domain in which user goals are spatial trajectories across a landscape. The disparity between the representation of spatial routes as raw pixel coordinates and their grounding as semantic utterances creates an interesting challenge compared to conventional slot-filling domains. We derive a feature-based representation of spatial goals which facilitates reasoning and admits generalisation to new routes not encountered at training time. The probabilistic formulation of our model allows us to capture variability of behaviour given the same underlying goal, a property frequently exhibited by human users in the domain. We first evaluate intrinsically using held-out probability and perplexity, and find a substantial reduction in uncertainty brought by our spatial representation. We further evaluate extrinsically in a human judgement task and find that our model’s behaviour does not differ significantly from the behaviour of real users. We conclude by sketching two novel ideas for future work: the first is to deploy the user models as transition functions for MDP-based dialog managers; the second is to use the models as a means of restricting the search space for optimal policies, by treating optimal behaviour as a subset of the (distributions over) plausible behaviour which we have induced.

Multimodal interfaces are becoming increasingly important with the advent of mobile devices, accessibility considerations, and novel software technologies that combine diverse interaction media. This thesis investigates systems support for web browsing in a multimodal interface. Specifically, we outline the design and implementation of a software framework that integrates hyperlink and voice interaction. This enables the user to engage in out-of-turn interactions to personalize access at an information site. For the developer, the framework enables the creation of sites that adapt to the needs of users, yet permits fine-grained control over what interactions to support. Design methodology, implementation details, and two case studies are presented. / Master of Science

近年來3D虛擬環境與語音界面(Voice User Interface)在個人電腦上的應用逐漸受到重視。說話是人類最自然的溝通方式，若能在虛擬環境中加入語音界面，將使人物間的互動更為流暢。近年來雖有許多研究致力於3D虛擬環境與語音界面的整合，但在多人環境中對話管理(Dialog Management)等相關問題上，一直缺乏有效的解決方案。本研究的主要目的，即在解決語音界面整合及對話管理等問題，並實現多人虛擬環境的語音互動機制。我們針對虛擬環境中語音與動畫同步、對話管理機制與多人環境中之語音處理機制等問題，設計一個以VoiceXML為基礎的XAML-V (eXtensible Animation Markup Language – Voice extension ) 語言，並將其實作結果於一個多人虛擬環境系統中驗証其可行性及有效性。 / The applications of 3D virtual environments and voice user interface (VUI) on personal computers have received significant attentions in recent years. Since speech is the most natural way of communication, incorporating VUI into virtual environments can enhance user interaction and immersiveness. Although there have been many researches addressing the issue of integrating VUI and 3D virtual environment, most of the proposed solutions do not provide an effective mechanism for multi-user dialog management. The objective of this research is on providing a solution for VUI integration and dialog management and realizing such a mechanism in a multi-user virtual environment. We have designed a dialog scripting language called XAML-V (eXtensible Animation Markup Language – Voice Extension), based on the VoiceXML standard, to address the issues of synchronization between VUI and animation and dialog management for multi-user interaction. We have also implemented such a language and realized it on a multi-user virtual environment to evaluate the effectiveness of this design.

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

El objetivo principal de la tesis que se presenta es el estudio y desarrollo de diferentes metodologías para la gestión del diálogo en sistemas de diálogo hablado. El principal reto planteado en la tesis reside en el desarrollo de metodologías puramente estadísticas para la gestión del diálogo, basadas en el aprendizaje de un modelo a partir de un corpus de diálogos etiquetados. En este campo, se presentan diferentes aproximaciones para realizar la gestión, la mejora del modelo estadístico y la evaluación del sistema del diálogo. Para la implementación práctica de estas metodologías, en el ámbito de una tarea específica, ha sido necesaria la adquisición y etiquetado de un corpus de diálogos. El hecho de disponer de un gran corpus de diálogos ha facilitado el aprendizaje y evaluación del modelo de gestión desarrollado. Así mismo, se ha implementado un sistema de diálogo completo, que permite evaluar el funcionamiento práctico de las metodologías de gestión en condiciones reales de uso. Para evaluar las técnicas de gestión del diálogo se proponen diferentes aproximaciones: la evaluación mediante usuarios reales; la evaluación con el corpus adquirido, en el cual se han definido unas particiones de entrenamiento y prueba; y la utilización de técnicas de simulación de usuarios. El simulador de usuario desarrollado permite modelizar de forma estadística el proceso completo del diálogo. En la aproximación que se presenta, tanto la obtención de la respuesta del sistema como la generación del turno de usuario se modelizan como un problema de clasificación, para el que se codifica como entrada un conjunto de variables que representan el estado actual del diálogo y como resultado de la clasificación se obtienen las probabilidades de seleccionar cada una de las respuestas (secuencia de actos de diálogo) definidas respectivamente para el usuario y el sistema. / Griol Barres, D. (2007). Desarrollo y evaluación de diferentes metodologías para la gestión automática del diálogo [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1956