The RavenClaw dialog management framework: Architecture and systems

Bohus, Dan; Rudnicky, Alexander I.

doi:10.1016/j.csl.2008.10.001

Cited by 222 publications

(177 citation statements)

References 26 publications

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“…The Olympus/RavenClaw pipeline architecture [3,4] has been the framework for at least a dozen SDSs. The components in an Olympus/Ravenclaw SDS communicate through the Galaxy hub [5].…”

Section: Spoken Dialogue Systemsmentioning

confidence: 99%

Data Mining to Support Human-Machine Dialogue for Autonomous Agents

Epstein

Passonneau

Ligorio

et al. 2012

Lecture Notes in Computer Science

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Abstract. Next-generation autonomous agents will be expected to converse with people to achieve their mutual goals. Human-machine dialogue, however, is challenged by noisy acoustic data, and by people's preference for more natural interaction. This paper describes an ambitious project that embeds human subjects in a spoken dialogue system. It collects a rich and novel data set, including spoken dialogue, human behavior, and system features. During data collection, subjects were restricted to the same databases, action choices, and noisy automated speech recognition output as a spoken dialogue system. This paper mines that data to learn how people manage the problems that arise during dialogue under such restrictions. Two different approaches to successful, goal-directed dialogue are identified this way, from which supervised learning can predict appropriate dialogue choices. The resultant models can then be incorporated into an autonomous agent that seeks to assist its user.

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“…The Olympus/RavenClaw pipeline architecture [3,4] has been the framework for at least a dozen SDSs. The components in an Olympus/Ravenclaw SDS communicate through the Galaxy hub [5].…”

Section: Spoken Dialogue Systemsmentioning

confidence: 99%

Data Mining to Support Human-Machine Dialogue for Autonomous Agents

Epstein

Passonneau

Ligorio

et al. 2012

Lecture Notes in Computer Science

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“…Meanwhile, various dialogue authoring frameworks have been developed in academia to facilitate the development of dialogue systems by authoring state update rules (Bohus and Rudnicky, 2009; P. Lison, 2015). In particular, OpenDial, an open source dialogue system framework based on a information-state architecture, allows system developers to easily specify and edit dialogue be-216 haviours, which is a crucial requirement for commercial conversational applications.…”

Section: Introductionmentioning

confidence: 99%

“…While authoring a system-initiative dialogue is quick and easy to maintain, authoring a user-initiative dialogue system in VoiceXML often results in large interdependent code bases that are increasingly difficult to maintain. Furthermore, these dialogue systems cannot capture multiple state hypotheses nor optimise the dialogue by learning from previous interactions.Meanwhile, various dialogue authoring frameworks have been developed in academia to facilitate the development of dialogue systems by authoring state update rules (Bohus and Rudnicky, 2009; P. Lison, 2015). In particular, OpenDial, an open source dialogue system framework based on a information-state architecture, allows system developers to easily specify and edit dialogue be-216 haviours, which is a crucial requirement for commercial conversational applications.…”

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confidence: 99%

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Katagiri

Nakano

Fernández

et al. 2016

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We extend special thanks to our Local co-Chairs, Ron Artstein and Alesia Gainer, and their team of student volunteers. We know SIGDIAL 2016 would not have been possible without Ron and Alesia, who invested so much effort in arranging the conference venue and accommodations, handling registration, making banquet arrangements, and handling numerous other preparations for the conference. The student volunteers for on-site assistance also deserve our appreciation.Ethan Selfridge, Sponsorships Chair, has earned our appreciation for recruiting and liaising with our conference sponsors, many of whom continue to contribute year after year. Sponsorships support valuable aspects of the program, such as the invited speakers and conference banquet. In recognition of this, we gratefully acknowledge the support of our sponsors: (Platinum level) Microsoft Research, Xerox and PARC, Intel, (Gold level) Facebook, (Silver level) Amazon Alexa, Interactions, Educational Testing Service, Honda Research Institute, and Yahoo!. At the same time, we thank Priscilla Rasmussen at the ACL for tirelessly handling the financial aspects of sponsorship for SIGDIAL 2016, and for securing our ISBN.iii We also thank the SIGdial board, especially officers Amanda Stent, Jason Williams and Kristiina Jokinen for their advice and support from beginning to end.Finally, we thank all the authors of the papers in this volume, and all the conference participants for making this stimulating event a valuable opportunity for growth in the research areas of discourse and dialogue. AbstractThis paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state. IntroductionTask-oriented dialog systems have been an important branch of spoken dialog system (SDS) research (Raux et al., 2005; Young, 2006; Bohus and Rudnicky, 2003). The SDS agent has to achieve some predefined targets (e.g. booking a flight) through natural language interaction with the users. The typical structure of a task-oriented dialog system is outlined in Figure 1 (Young, 2006). This pipeline consists of several independently-developed modules: natural language understanding (the NLU) maps the user utterances to some semantic representation. This information is further processed by the dialog state tracker (DST), which accumulates the input of the turn along with the dialog history. The DST outputs the current dialog state and the dialog policy selects the next system action based on the dialog state. Then natural language gene...

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“…Dialog management is performed by RavenClaw [11]. The RavenClaw dialog management framework enforces a clear separation between the domain-specific and the domain-independent aspects of the dialog control logic.…”

Section: Dialog Managementmentioning

confidence: 99%

Affective effects of speech-enabled robots for language learning

Lee

Kim

Lee

et al. 2010

2010 IEEE Spoken Language Technology Workshop

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This study introduces the speech and language technologies used in the educational assistant robots that we developed for language learning and exploring the affective effects of robot-assisted language learning (RALL). To achieve this purpose, a course was designed in which students have meaningful interaction with intelligent robots in an immersive environment. A total of 24 elementary students, ranging in age over 9-13, were enrolled in English lessons. Descriptive statistics and pre-test/post-test design were used to investigate the affective effects of RALL approach. The result showed that RALL is promoting and improving students" satisfaction, interest, confidence, and motivation at the significance level of 0.01.

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The RavenClaw dialog management framework: Architecture and systems

Cited by 222 publications

References 26 publications

Data Mining to Support Human-Machine Dialogue for Autonomous Agents

Data Mining to Support Human-Machine Dialogue for Autonomous Agents

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Affective effects of speech-enabled robots for language learning

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