Generic dialogue modeling for multi-application dialogue systems

We present a novel approach to developing interfaces for multi-application dialogue systems. The targeted interfaces allow transparent switching between a large number of applications within one system. The approach, based on the Rapid Dialogue Prototyping Methodology (RDPM) and the Vector Space model techniques from Information Retrieval, is composed of three main steps: (1) producing finalized dia logue models for applications using the RDPM, (2) designing an application interaction hierarchy, and (3) navigating between the applications based on the user's application of interest

Zero-Shot Learning for Semantic Utterance Classification

We propose a novel zero-shot learning method for semantic utterance classification (SUC). It learns a classifier $f: X \to Y$ for problems where none of the semantic categories $Y$ are present in the training set. The framework uncovers the link between categories and utterances using a semantic space. We show that this semantic space can be learned by deep neural networks trained on large amounts of search engine query log data. More precisely, we propose a novel method that can learn discriminative semantic features without supervision. It uses the zero-shot learning framework to guide the learning of the semantic features. We demonstrate the effectiveness of the zero-shot semantic learning algorithm on the SUC dataset collected by (Tur, 2012). Furthermore, we achieve state-of-the-art results by combining the semantic features with a supervised method

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Automatic analysis of medical dialogue in the home hemodialysis domain : structure induction and summarization

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 129-134).Spoken medical dialogue is a valuable source of information, and it forms a foundation for diagnosis, prevention and therapeutic management. However, understanding even a perfect transcript of spoken dialogue is challenging for humans because of the lack of structure and the verbosity of dialogues. This work presents a first step towards automatic analysis of spoken medical dialogue. The backbone of our approach is an abstraction of a dialogue into a sequence of semantic categories. This abstraction uncovers structure in informal, verbose conversation between a caregiver and a patient, thereby facilitating automatic processing of dialogue content. Our method induces this structure based on a range of linguistic and contextual features that are integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). We demonstrate the utility of this structural abstraction by incorporating it into an automatic dialogue summarizer. Our evaluation results indicate that automatically generated summaries exhibit high resemblance to summaries written by humans and significantly outperform random selections (p<0.0001) in precision and recall.(cont.) In addition, task-based evaluation shows that physicians can reasonably answer questions related to patient care by looking at the automatically-generated summaries alone, in contrast to the physicians' performance when they were given summaries from a naive summarizer (p<0.05). This is a significant result because it spares the physician from the need to wade through irrelevant material ample in dialogue transcripts. This work demonstrates the feasibility of automatically structuring and summarizing spoken medical dialogue.by Ronilda Covar Lacson.Ph.D

Generando instrucciones de navegación peatonal usando generación por selección

Tesis (Lic. en Ciencias de la Computación)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía y Física, 2014.En este trabajo describimos un método para desarrollar un instructor virtual de navegación peatonal basado en interacciones reales entre humanos. Un instructor virtual es un agente capaz de cumplir el rol de un instructor humano, y su objetivo es asistir a un usuario humano a llevar a cabo diferentes tareas dentro del contexto de una ciudad real. Para poder cumplir con su trabajo, un instructor virtual necesita poseer la capacidad de describir lo que necesita hacerse de una manera efectiva, considerando las características del entorno virtual en el cual se encuentra, y logrando comprometer al usuario en la tarea propuesta. El instructor presentado se comunica utilizando un algoritmo de generación por selección, basado en un corpus de interacciones reales anotado previamente de forma automática, generado dentro del mundo de interés. Este sistema es resistente a errores de interpretación por parte del usuario, está al tanto de la ubicación del usuario constantemente, y usa diferentes puntos de referencia de la ciudad para guiarlo. El instructor fue evaluado con usuarios reales de forma situada en Street View de Google en la ciudad de Edimburgo en la plataforma provista por la competencia internacional GRUVE. El instructor generado fue superior al baseline provisto por la competencia en diversas métricas, como naturalidad y efectividad de las instrucciones, entre otras.In this work we describe a method to develop a virtual instructor for pedestrian navigation based on real interactions between a human instructor and a human pedestrian. A virtual instructor is an agent capable of fulfilling the role of a human instructor,and its goal is to assist a pedestrian in the accomplishment of different tasks within the context of a real city. In order to guide a user while performing a task, an effective instructor knows how to describe what needs to be done in a way that accounts for the nuances of the virtual environment and that is good enough to engage the trainee or the gamer in the activity. The instructor decides what to say using a generation by selection algorithm, based on a automatically annotated corpus of real interactions generated within the world of interest, and is able to react to different requests by the pedestrian. The instructor can deal with interpretation errors made by the user, is constantly aware of the pedestrian position, and it can use different city landmarks to guide him. The instructor was evaluated with real users through Google Street View, located in the city of Edinburgh, by means of the platform provided by the international challenge GRUVE. The generated instructor was superior to the baseline system provided by this challenge in various metrics, such as naturalness and effectiveness of instructions, among others.Fil: Avalos Ambroggio, Santiago Eugenio. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física; Argentina