Four Mode Based Dialogue Management with Modified POMDP Model

This thesis proposes a method to manage the interaction between the user and the system dynamically, through speech or text input which updates the user goals, select system actions and calculate rewards for each system response at each time-stamp. The main focus is made on the dialog manager, which decides how to continue the dialogue. We have used POMDP technique, as it maintains a belief distribution on the dialogue states based on the observations over the dialogue even in a noisy environment. Four contextual control modes are introduced in dialogue management for decision-making mechanism, and to keep track of machine behaviour for each dialogue state. The result obtained proves that our proposed framework has overcome the limitations of prior POMDP methods, and exactly understands the actual intention of the users within the available time, providing very interactive conversation between the user and the computer

Sistemas de diálogo: una revisión

Spoken dialogue systems are computer programs developed to interact with users employing speech in order to provide them with specific automated services. The interaction is carried out by means of dialogue turns, which in many studies available in the literature, researchers aim to make as similar as possible to those between humans in terms of naturalness, intelligence and affective content. In this paper we describe the fundaments of these systems including the main technologies employed for their development. We also present an evolution of this technology and discuss some current applications. Moreover, we discuss development paradigms, including scripting languages and the development of conversational interfaces for mobile apps. The correct modelling of the user is a key aspect of this technology. This is why we also describe affective, personality and contextual models. Finally, we address some current research trends in terms of verbal communication, multimodal interaction and dialogue management.Los sistemas de diálogo son programas de ordenador desarrollados para interaccionar con los usuarios mediante habla, con la finalidad de proporcionarles servicios automatizados. La interacción se lleva a cabo mediante turnos de un tipo de diálogo que, en muchos estudios existentes en la literatura, los investigadores intentan que se parezca lo más posible al diálogo real que se lleva a cabo entre las personas en lo que se refiere a naturalidad, inteligencia y contenido afectivo. En este artículo describimos los fundamentos de esta tecnología, incluyendo las tecnologías básicas que se utilizan para implementar este tipo de sistemas. También presentamos una evolución de la tecnología y comentamos algunas aplicaciones actuales. Asimismo, describimos paradigmas de interacción, incluyendo lenguajes de script y desarrollo de interfaces conversacionales para aplicaciones móviles. Un aspecto clave de esta tecnología consiste en realizar un correcto modelado del usuario. Por este motivo, discutimos diversos modelos afectivos, de personalidad y contextuales. Finalmente, comentamos algunas líneas de investigación actuales relacionadas con la comunicación verbal, interacción multimodal y gestión del diálogo

Development of customized conversational interfaces with Deep Learning techniques

This Bachelor’s thesis will cover the end-to-end process of developing a personalized conversational interface for a specific domain, using Deep Learning techniques. In particular, it will focus on the study of the Dialog Manager module, which is in charge of deciding the next system response based on the current dialog state. AlthoughthereisplentyofliteratureaboutMachineLearningappliedtotheconstruction of dialog management models, there is very little reference to the utilization of Deep Learning for such task. As a result, this work analyzes the improvement that deep neural networks can bring to accuracy. Several models are created with TensorFlow, and comparisons are made with traditional Machine Learning solutions. Results show that Deep Learning is not the most recommended approach for this type of problems, yet further research is suggested for more complex datasets. After this, one of the Deep Learning models, based on a train scheduling domain, is used for the implementation of the dialog manager inside a real spoken dialog system. To integrate the rest of required components of such technology (automatic speech recognizer, natural language understanding module and text-to-speech service), a modern framework is used: DialogFlow. With this platform, a complete chatbot is built in the form of an assistant in the train scheduling domain. Evaluationof thespoken dialogsystemwith real users generatesavery positivefeedback, demonstrating that a Deep Learning based dialog manager is a valid solution in commercial conversational interfaces.Ingeniería Informátic

Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016

These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions

Human-Computer Interaction

In this book the reader will find a collection of 31 papers presenting different facets of Human Computer Interaction, the result of research projects and experiments as well as new approaches to design user interfaces. The book is organized according to the following main topics in a sequential order: new interaction paradigms, multimodality, usability studies on several interaction mechanisms, human factors, universal design and development methodologies and tools

Natural language interfaces to relational databases

Máster Universitario en Lógica, Computación e Inteligencia Artificia

Extracting Information from Spoken User Input:A Machine Learning Approach

We propose a module that performs automatic analysis of user input in spoken dialogue systems using machine learning algorithms. The input to the module is material received from the speech recogniser and the dialogue manager of the spoken dialogue system, the output is a four-level pragmatic-semantic representation of the user utterance. Our investigation shows that when the four interpretation levels are combined in a complex machine learning task, the performance of the module is significantly better than the score of an informed baseline strategy. However, via a systematic, automatised search for the optimal subtask combinations we can gain substantial improvement produced by both classifiers for all four interpretation subtasks. A case study is conducted on dialogues between an automatised, experimental system that gives information on the phone about train connections in the Netherlands, and its users who speak in Dutch. We find that drawing on unsophisticated, potentially noisy features that characterise the dialogue situation, and by performing automatic optimisation of the formulated machine learning task it is possible to extract sophisticated information of practical pragmatic-semantic value from spoken user input with robust performance. This means that our module can with a good score interpret whether the user of the system is giving slot-filling information, and for which query slots (e.g., departure station, departure time, etc.), whether the user gave a positive or a negative answer to the system, or whether the user signals that there are problems in the interaction.