Focus tree: modeling attentional information in task-oriented human-machine interaction

Gnjatović, Milan; Janev, Marko; Delić, Vlado

doi:10.1007/s10489-011-0329-5

Cited by 30 publications

(18 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the proposed approach is applicable to any interaction domain that can be modelled by a focus tree. Gnjatović and Delić 14 and Gnjatović et al 16 argue that the focus tree model provides a scalable and domain-independent approach to meaning representation in task-oriented human-machine interaction that is sufficiently general both from the engineering and linguistic points of view. At the architectural level, the proposed algorithm for evaluation of recognition hypotheses can be applied to an arbitrary speech recognition system.…”

Section: Resultsmentioning

confidence: 99%

Hybrid methodological approach to context-dependent speech recognition

Miskovic

Gnjatović

Štrbac

et al. 2017

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human-machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

show abstract

Section: Resultsmentioning

confidence: 99%

Hybrid methodological approach to context-dependent speech recognition

Miskovic

Gnjatović

Štrbac

et al. 2017

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to Zukerman and Litman [168], very early examples of user modeling in these field are dominated by knowledge-based formalisms and various types of logic aimed at modeling the complex beliefs and intentions of agents [27,46,103,133]. Grosz and Sidner analyzed the discourse structure as composed of the linguistic structure, the intentional structure, and an attentional state [58,65].…”

Section: Modeling the User Intentionmentioning

confidence: 99%

Modeling the user state for context-aware spoken interaction in ambient assisted living

2014

View full text Add to dashboard Cite

Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfil these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influenc of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.

show abstract

“…This paper builds upon the focus tree model-a cognitively-inspired, representational model of attentional information in machine dialogue introduced in [7], [8], [9]. It has been demonstrated 2 that this model allows for robust processing of spontaneously uttered user's commands (without a preset grammar) and designing of domain-independent dialogue strategies.…”

Section: B Previous Workmentioning

confidence: 99%

“…• Natural language understanding-As part of previous work, this model introduced an algorithm for robust processing of spontaneously produced user's commands without explicit syntactic expectations [7], [8], enabling the system to process the user's dialogue acts in a uniform manner, for any given focus tree and vocabulary.…”

Section: B Prototype Systemmentioning

confidence: 99%

Changing concepts of machine dialogue management

Gnjatović

2014

2014 5th IEEE Conference on Cognitive Infocommunications (CogInfoCom)

View full text Add to dashboard Cite

An important research question in machine dialogue management is how to go beyond hand-crafted approaches. Currently, statistical approaches are prevalent. However, recently, researchers have focused again on the representational capacities of the human language processing system. The point of departure for this paper is that this methodological shift is necessary if we aim at designing advanced conversational agents. In line with this, the paper introduces an extension of the focus tree model of attentional information in machine dialogue. It proposes an approach to automatic construction of a focus tree for a given interaction domain. In addition, the paper provides a brief insight into previous work on this model in order to report on a representational, cognitively-inspired and domain-independent approach to machine dialogue management. Finally, the paper discusses selected aspects of a prototype conversational agent designed to maintain natural language interaction in therapeutic settings between the therapist and the robot.

show abstract

Focus tree: modeling attentional information in task-oriented human-machine interaction

Cited by 30 publications

References 40 publications

Hybrid methodological approach to context-dependent speech recognition

Hybrid methodological approach to context-dependent speech recognition

Modeling the user state for context-aware spoken interaction in ambient assisted living

Changing concepts of machine dialogue management

Contact Info

Product

Resources

About