Multimodal agent interfaces and system architectures for health and fitness companions

Multimodal conversational spoken dialogues using physical and virtual agents provide a potential interface to motivate and support users in the domain of health and fitness. In this paper we present how such multimodal conversational Companions can be implemented to support their owners in various pervasive and mobile settings. In particular, we focus on different forms of multimodality and system architectures for such interfaces

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

THE ROLE OF COGNITIVE APPORTIONMENT IN INFORMATION SYSTEMS

As the number of information system users increases, we are witnessing a related increase in the complexity and the diversity of their applications. The increasing functional complexity amplifies the degree of functional and technical understanding required of the user to make productive use of the application tools. Emerging technologies, increased and varied user interests and radical changes in the nature of applications give rise to the opportunity and necessity to re-examine the proper apportionment of cognitive responsibilities in human/system interaction. Examples illustrate the opportunities afforded by such an examination. A framework is presented that illustrates many of the tradeoffs that occur in a reapportionment activity. A knowledge-based architecture is proposed to facilitate both static and dynamic reapportionment decisions

Providing personalized Internet services by means of context-aware spoken dialogue systems

The widespread use of new mobile technology implementing wireless communications enables a new type of advanced applications to access information services on the Internet. In order to provide services which meet the user needs through intelligent information retrieval, the system must sense and interpret the user environment and the communication context. Though context-awareness is vital to provide services adapted to the user preferences, it cannot be useful if such services are difficult to access. The development of spoken dialogue systems for these applications facilitates interaction in natural language with the environment which is also benefited from contextual information. In this paper, we propose a framework to develop context-aware dialogue systems that dynamically incorporate user specific requirements and preferences as well as characteristics about the interaction environment, in order to improve and personalize web information and services. We have identified the major components for context-aware dialogue systems and placed them within a general-purpose architecture. The framework also describes a representation mode based on a dialogue register in order to share information between the elements of the architecture, and incorporates statistical methodologies for dialogue management in order to reduce the effort required for both the implementation of a new system and the adaptation to a new task. We have evaluated our proposal developing a travel-planning system, and provide a detailed discussion of its positive influence in the quality of the interaction and the information and services provided.Research funded by projects CICYT TIN2011- 28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485), and DPS2008- 07029-C02-02.Publicad

Do (and say) as I say: Linguistic adaptation in human-computer dialogs

© Theodora Koulouri, Stanislao Lauria, and Robert D. Macredie. This article has been made available through the Brunel Open Access Publishing Fund.There is strong research evidence showing that people naturally align to each other’s vocabulary, sentence structure, and acoustic features in dialog, yet little is known about how the alignment mechanism operates in the interaction between users and computer systems let alone how it may be exploited to improve the efficiency of the interaction. This article provides an account of lexical alignment in human–computer dialogs, based on empirical data collected in a simulated human–computer interaction scenario. The results indicate that alignment is present, resulting in the gradual reduction and stabilization of the vocabulary-in-use, and that it is also reciprocal. Further, the results suggest that when system and user errors occur, the development of alignment is temporarily disrupted and users tend to introduce novel words to the dialog. The results also indicate that alignment in human–computer interaction may have a strong strategic component and is used as a resource to compensate for less optimal (visually impoverished) interaction conditions. Moreover, lower alignment is associated with less successful interaction, as measured by user perceptions. The article distills the results of the study into design recommendations for human–computer dialog systems and uses them to outline a model of dialog management that supports and exploits alignment through mechanisms for in-use adaptation of the system’s grammar and lexicon

Evaluation of a hierarchical reinforcement learning spoken dialogue system

We describe an evaluation of spoken dialogue strategies designed using hierarchical reinforcement learning agents. The dialogue strategies were learnt in a simulated environment and tested in a laboratory setting with 32 users. These dialogues were used to evaluate three types of machine dialogue behaviour: hand-coded, fully-learnt and semi-learnt. These experiments also served to evaluate the realism of simulated dialogues using two proposed metrics contrasted with ‘Precision-Recall’. The learnt dialogue behaviours used the Semi-Markov Decision Process (SMDP) model, and we report the first evaluation of this model in a realistic conversational environment. Experimental results in the travel planning domain provide evidence to support the following claims: (a) hierarchical semi-learnt dialogue agents are a better alternative (with higher overall performance) than deterministic or fully-learnt behaviour; (b) spoken dialogue strategies learnt with highly coherent user behaviour and conservative recognition error rates (keyword error rate of 20%) can outperform a reasonable hand-coded strategy; and (c) hierarchical reinforcement learning dialogue agents are feasible and promising for the (semi) automatic design of optimized dialogue behaviours in larger-scale systems