Where Do We Go from Here?

Pieraccini, Roberto; Huerta, Juan M.

doi:10.1007/978-1-4020-6821-8_1

Cited by 15 publications

(5 citation statements)

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“…Such applications follow a structured conversation approach. Despite being restrictive and having built on constrained vocabulary, directed dialog can have a much higher usability and improvement in text recognition rates [16]. Moreover, with directed conversation strategy, users will not be lost, and would be aware of system's expected input and capabilities.…”

Section: Related Work and Chatbot Preliminariesmentioning

confidence: 99%

Performance evaluation of keyword detection for the chatbot model

Gunnam¹,

Inupakutika²,

Mundlamuri³

et al. 2023

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To enable enriched free-text human-computer conversations, keyword detection is an important component in chatbot models as it helps to identify specific keywords in user inputs that can trigger the chatbot to respond in a certain way. The performance of keyword detection thus depends on several factors such as the quality and quantity of training data, the selection of apt learning algorithms, and the tuning of various parameters. The performance evaluation of keyword detection for a chatbot involves taking into consideration, the specific requirements of the chatbot and the expected usage patterns of its users. To that end, this work investigates the keyword detection performance with limited vocabulary in the closed-domain chatbot model. A keyword reduction methodology is presented and experimental results on one public and one custom closed-domain datasets indicate about 4.6% improvement in F1-score and comparable performance respectively.

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Section: Related Work and Chatbot Preliminariesmentioning

confidence: 99%

Performance evaluation of keyword detection for the chatbot model

Gunnam¹,

Inupakutika²,

Mundlamuri³

et al. 2023

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show abstract

“…For instance, when the first speech recognizers and synthesizers were accessible, it was a huge advance for researchers and practitioners that centred on dialogue management, as they could focus on the aspects directly related to handling the conversation without worrying about the details of how to recognize the user input or synthesize the output. Pieraccini and Huerta (2008) highlighted the importance of "reusable components" as one of the main trends for the industry of dialogue systems, as it was and still is an important aspect to build increasingly complex applications by taking advantage of already existing modules.…”

Section: Development Paradigmsmentioning

confidence: 99%

Review of spoken dialogue systems

López-Cózar

Callejas

Griol

et al. 2014

loquens

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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.Keywords: dialogue; language understanding; dialogue management; natural language generation; speech synthesis. RESUMEN:Sistemas de diálogo: una revisión.-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.Palabras clave: diálogo; comprensión del lenguaje; gestión del diálogo; generación del lenguaje natural; síntesis del habla.

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“…The natural language and dialogue technology that powers virtual humans is varied and often selected or developed to match the characteristics of particular applications. If the interaction between the virtual human and human user is tightly scripted, such that user utterances are restricted to a finite set of choices that is presented by the system, approaches such as finite state control using call flow graphs (Pieraccini & Huerta, 2005) and branching narrative for interactive games (Tavinor, 2009) are sufficient. On the other hand, if more varied natural language input is to be expected by the virtual human, one general technique that is widely used and is well matched for virtual humans that provide information by answering questions about a certain topic (e.g.…”

Section: Artificial Intelligence Dialogue Systemsmentioning

confidence: 99%

Sorting Out the Virtual Patient

Talbot

Sagae

John

et al. 2012

International Journal of Gaming and Computer-Mediated Simulations

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Since Dr. Howard Barrows (1964) introduced the human standardized patient in 1963, there have been attempts to game a computer-based simulacrum of a patient encounter; the first being a heart attack simulation using the online PLATO system (Bitzer, 1966). With the now ubiquitous use of computers in medicine, interest and effort have expended in the area of Virtual Patients (VPs). One problem in trying to understand VPs is that there are several quite distinct educational approaches that are all called a ‘virtual patient.’ This article is not a general review of virtual patients as current reviews of excellent quality exist (Poulton & Balasubramaniam, 2011; Cook & Triola, 2009). Also, research that demonstrates the efficacy of virtual patients is ample (Triola, et al., 2006). This article assesses the different kinds of things the authors call “virtual patients”, which are often mutually exclusive approaches, then analyzes their interaction structure or ‘game-play’, and considers the best use scenarios for that design strategy. This article also explores dialogue-based conversational agents as virtual patients and the technology approaches to creating them. Finally, the authors offer a theoretical approach that synthesizes several educational approaches over the course of a medical encounter and recommend the optimal technology for the type of encounter desired.

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Where Do We Go from Here?

Cited by 15 publications

References 0 publications

Performance evaluation of keyword detection for the chatbot model

Performance evaluation of keyword detection for the chatbot model

Review of spoken dialogue systems

Sorting Out the Virtual Patient

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