Resolution of Referential Ambiguity in Human-Robot Dialogue Using Dempster-Shafer Theoretic Pragmatics

Williams, Tom; Scheutz, Matthias

doi:10.15607/rss.2017.xiii.017

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…Natural-language dialogue enables robots to ask clarification questions [30,59,84] and provide status updates [60,115] to conversational partners. Some of this research contributes to disambiguation methodologies [37,56,121], but a few attempt to renegotiate natural-language instructions that are not possible to execute [24,77,90]. In comparison, TeamTalk uses a data-driven approach to handle referential ambiguity and impossible tasks.…”

Section: Dialogue Systems To Support Hrimentioning

confidence: 99%

Miscommunication Detection and Recovery in Situated Human–Robot Dialogue

Marge

Rudnicky

2019

ACM Trans. Interact. Intell. Syst.

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Even without speech recognition errors, robots may face difficulties interpreting natural-language instructions. We present a method for robustly handling miscommunication between people and robots in task-oriented spoken dialogue. This capability is implemented in TeamTalk, a conversational interface to robots that supports detection and recovery from the situated grounding problems of referential ambiguity and impossible actions. We introduce a representation that detects these problems and a nearest-neighbor learning algorithm that selects recovery strategies for a virtual robot. When the robot encounters a grounding problem, it looks back on its interaction history to consider how it resolved similar situations. The learning method is trained initially on crowdsourced data but is then supplemented by interactions from a longitudinal user study in which six participants performed navigation tasks with the robot. We compare results collected using a general model to user-specific models and find that user-specific models perform best on measures of dialogue efficiency, while the general model yields the highest agreement with human judges. Our overall contribution is a novel approach to detecting and recovering from miscommunication in dialogue by including situated context, namely, information from a robot's path planner and surroundings. CCS Concepts: • Computing methodologies → Discourse, dialogue and pragmatics; • Humancentered computing → Natural language interfaces;

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Section: Dialogue Systems To Support Hrimentioning

confidence: 99%

Miscommunication Detection and Recovery in Situated Human–Robot Dialogue

Marge

Rudnicky

2019

ACM Trans. Interact. Intell. Syst.

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“…How to best enable robots to ask questions has been studied at least since Fong et al's Robot, Asker of Questions, 13 but only recently have researchers sought to enable robust clarification request generation. [14][15][16] These works seek to respond to commands such as "Bring me the ball" with utterances such as "Do you mean the red ball or the blue ball? "…”

Section: Clarification Request Generationmentioning

confidence: 99%

Robot: Asker of questions and changer of norms?

2018

Hybrid Worlds: Societal and Ethical Challenges

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Recent work in behavioral ethics has brought to light the role that technologies play in shaping human ethics. Language-capable autonomous robots are uniquely positioned to impact human ethics. It is critical to identify and mitigate negative consequences of this technology on human morality, as robots will likely be deployed in increasingly ethically significant contexts over time. We argue that the current status quo for dialogue systems in autonomous agents can (1) cause robots to unintentionally miscommunicate their ethical intentions, and (2) weaken humans' contextual application of moral norms.

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“…During the reference resolution process, when the property box(X) is determined to hold for each entity, it is placed into that entity's STM Buffer within the simulated Vision Component. Because the expression is ambiguous, a clarification request is automatically generated [41] to determine whether object 1 or object 2 is the target referent. For each of these entities, SD-PIA is recruited to generate referring expressions.…”

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confidence: 99%

Augmenting Robot Knowledge Consultants with Distributed Short Term Memory

et al. 2018

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Human-robot communication in situated environments involves a complex interplay between knowledge representations across a wide variety of modalities. Crucially, linguistic information must be associated with representations of objects, locations, people, and goals, which may be represented in very different ways. In previous work, we developed a Consultant Framework that facilitates modality-agnostic access to information distributed across a set of heterogeneously represented knowledge sources. In this work, we draw inspiration from cognitive science to augment these distributed knowledge sources with Short Term Memory Buffers to create an STM-augmented algorithm for referring expression generation. We then discuss the potential performance benefits of this approach and insights from cognitive science that may inform future refinements in the design of our approach.

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Resolution of Referential Ambiguity in Human-Robot Dialogue Using Dempster-Shafer Theoretic Pragmatics

Cited by 13 publications

References 21 publications

Miscommunication Detection and Recovery in Situated Human–Robot Dialogue

Miscommunication Detection and Recovery in Situated Human–Robot Dialogue

Robot: Asker of questions and changer of norms?

Augmenting Robot Knowledge Consultants with Distributed Short Term Memory

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