Application of Verbal Intelligence in Dialog Systems for Multimodal Interaction
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In this paper we present a prototypical dialog system adaptive to verbal user intelligence. Verbal intelligence (VI) is the ability to analyze information and to solve problems using language-based reasoning. VI can be analyzed by the number of reused words, lemmas, n-grams, cosine similarity and other features. Here we concentrate on the application of VI in a human-computer interaction (HCI) and how this value can be used by the dialog management to adapt the dialog flow, and complexity at run-time. In our work complexity as well as informative value of presented information can be reduced or increased when encountering human-computer interaction by individually adapting to a lower or higher verbally intelligent user. Especially in intelligent environments, where user's may rely on speech as their primary interaction modality, the adaptation of system instructions to the the user's VI could prove helpful.Keywords:
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The emergence of smart devices and recent advances in spoken language technology are currently extending the use of conversational interfaces and spoken interaction to perform many tasks. The dialog management task of a conversational interface consists of selecting the next system response considering the user's actions, the dialog history, and the results of accessing the data repositories. In this paper we describe a dialog management technique adapted to multi-task conversational systems. In our proposal, specialized dialog models are used to deal with each specific subtask of dialog objective for which the dialog system has been designed. The practical application of the proposed technique to develop a dialog system acting as a customer support service shows that the use of these specialized dialog models increases the quality and number of successful interactions with the system in comparison with developing a single dialog model.
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Many of current spoken dialog systems can conduct non-task-oriented dialog. The systems that can improve user impression are required for users to keep using them. This paper focuses on self-disclosure, that is a process that a person reveals information about herself/himself to an interlocutor in human-human conversation. It is known that the self-disclosure plays a vital role to develop an intimate relationship. However, it is still unclear how exchanging the self-disclosures affects the user impression in the human-machine dialog. In this paper, we conduct dialog experiments to investigate the effectiveness of mutual self-disclosures between the user and the system. To achieve this goal, we built a spoken dialog system which conducts the dialog that the user and the system disclose information about themselves alternately. The dialog experiments revealed that the proposed system can improve the user impression regarding satisfaction and friendliness.
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Recently, spoken dialog systems using speech recognition technology have been becoming popular. Such as chat-like dialog systems, these systems which do not have any specific purpose are called "Non-Task-oriented spoken dialog system". In this study, we focused on non-task-oriented spoken dialog systems. We have developed a multi-party chat-like dialog system with different preferences between two agents. In order to provide more enjoyable chatting to users, we focused on the relationship between two agents. In this study, we dealt with two types of the chat-like dialog system with two agents: an "Unsympathetic dialog system "where one agent's preference differs from another agent, and a "Sympathetic dialog system" where both agents have the same preference. We evaluated user's degree of satisfaction by using our developed systems. As a result of the experiment, the sympathetic dialog system achieved a better impression in chatting. In order to solve the system problems obtained from the experimental results, we designed a new dialog system in order to deal with a more enjoyable conversation.
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This paper describes a dialog system allowing a human user and a personal assistant agent to communicate through natural language. The Assistant uses explicit task representation and knowledge representation to achieve an intelligent dialog. The dialog architecture is independent from the knowledge or task representation and includes different models: dialog models, task models, user models and domain models. The paper describes how the dialog manager uses the models to conduct the dialog and how the approach is related to other approaches in the same domain. The system has been implemented in a collaborative environment in order to personalize services and to improve interaction.
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