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Este trabalho apresenta um estudo sobre como a tecnologia adaptativa pode ser utilizada para aprimorar métodos existentes de gerenciamento de diálogo. O gerenciamento de diálogo é a atividade central em um sistema computacional de diálogo falado, sendo a responsável por decidir as ações comunicativas que devem ser enviadas ao usuário. Para evidenciar pontos que pudessem ser melhorados através do uso da tecnologia adaptativa, faz-se uma revisão literária ampla do gerenciamento do diálogo. Esta revisão também permite elencar critérios existentes e criar outros novos para avaliar gerenciadores de diálogos. Um modelo de gerenciamento adaptativo baseado em máquinas de estados, denominado Adaptalker, é então proposto e utilizado para criar um framework de desenvolvimento de gerenciadores de diálogo, o qual foi exercitado pelo desenvolvimento ilustrativo de uma aplicação simples de venda de pizzas. A análise desse exemplo permite observar como a adaptatividade é utilizada para aperfeiçoar o modelo, tornando-o capaz, por exemplo, de lidar de forma mais eficiente tanto com o reparo do diálogo quanto com a iniciativa do usuário. As regras de gerenciamento do Adaptalker são organizadas em submáquinas, que trabalham de forma concorrente para decidir qual a próxima ação comunicativa. / This work presents a study on how to apply adaptive technologies to improve existing dialog management methodologies. Dialog management is the central activity of a spoken dialog system, being responsible for choosing the communicative actions sent to the system user. In order to evidence parts that can be improved with adaptive technology, a large review on dialog management is presented. This review allows us to point existing criteria and create new ones to evaluate dialog managers. An adaptive management model based on finite state-based spoken dialog systems, Adaptalker, is proposed and used to build a development framework of dialog managers, which is illustrated by creating a pizza selling application. Analysis of this example allows us to observe how to use adaptivity to improve the model, allowing it to handle both dialog repair and user initiative more efficiently. Adaptalker groups its management rules in submachines that work concurrently to choose the next communication action.

Este trabalho apresenta um estudo sobre como a tecnologia adaptativa pode ser utilizada para aprimorar métodos existentes de gerenciamento de diálogo. O gerenciamento de diálogo é a atividade central em um sistema computacional de diálogo falado, sendo a responsável por decidir as ações comunicativas que devem ser enviadas ao usuário. Para evidenciar pontos que pudessem ser melhorados através do uso da tecnologia adaptativa, faz-se uma revisão literária ampla do gerenciamento do diálogo. Esta revisão também permite elencar critérios existentes e criar outros novos para avaliar gerenciadores de diálogos. Um modelo de gerenciamento adaptativo baseado em máquinas de estados, denominado Adaptalker, é então proposto e utilizado para criar um framework de desenvolvimento de gerenciadores de diálogo, o qual foi exercitado pelo desenvolvimento ilustrativo de uma aplicação simples de venda de pizzas. A análise desse exemplo permite observar como a adaptatividade é utilizada para aperfeiçoar o modelo, tornando-o capaz, por exemplo, de lidar de forma mais eficiente tanto com o reparo do diálogo quanto com a iniciativa do usuário. As regras de gerenciamento do Adaptalker são organizadas em submáquinas, que trabalham de forma concorrente para decidir qual a próxima ação comunicativa. / This work presents a study on how to apply adaptive technologies to improve existing dialog management methodologies. Dialog management is the central activity of a spoken dialog system, being responsible for choosing the communicative actions sent to the system user. In order to evidence parts that can be improved with adaptive technology, a large review on dialog management is presented. This review allows us to point existing criteria and create new ones to evaluate dialog managers. An adaptive management model based on finite state-based spoken dialog systems, Adaptalker, is proposed and used to build a development framework of dialog managers, which is illustrated by creating a pizza selling application. Analysis of this example allows us to observe how to use adaptivity to improve the model, allowing it to handle both dialog repair and user initiative more efficiently. Adaptalker groups its management rules in submachines that work concurrently to choose the next communication action.

Human behavior varies widely as does the design of spoken dialog systems (SDS). The search for predictors to match a user’s preference and efficiency for a specific dialog interface type in an SDS was the focus of this research. By using personality as described by the Five-Factor Method (FFM) and the Wizard of Oz technique for delivering three system initiatives of the SDS, participants interacted with each of the SDS initiatives in scheduling an airline flight. The three system initiatives were constructed as strict system, which did not allow the user control of the interaction; mixed system, which allowed the user some control of the interaction but with a system override; and user system, which allowed the user control of the interaction. In order to eliminate gender bias in using the FFM as the instrument, participants were matched in gender and age. Participants were 18 years old to 70 years old, passed a hearing test, had no disability that prohibited the use of the SDS, and were native English speakers. Participants completed an adult consent form, a 50-question personality assessment as described by the FFM, and the interaction with the SDS. Participants also completed a system preference indication form at the end of the interaction. Observations for efficiency were recorded on paper by the researcher. Although the findings did not show a definitive predictor for a SDS due to the small population sample, by using a multinomial regression approach to the statistical analysis, odds ratios of the data helped draw conclusions that support certain personality factors as important roles in a user’s preference and efficiency in choosing and using a SDS. This gives an area for future research. Also, the presumption that preference and efficiency always match was not supported by the results from two of the three systems. An additional area for future research was discovered in the gender data. Although not an initial part of the research, the data shows promise in predicting preference and efficiency for certain SDS. Future research is indicated.

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 no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1956 / Palancia