Search Results for author: Matthew Marge

Found 16 papers, 2 papers with code

A System For Robot Concept Learning Through Situated Dialogue

no code implementations SIGDIAL (ACL) 2022 Benjamin Kane, Felix Gervits, Matthias Scheutz, Matthew Marge

We evaluate the system by comparing learning efficiency to a human baseline in a collaborative reference resolution task and show that the system is effective and efficient in learning new concepts, and that it can informatively generate explanations about its behavior.

One-Shot Learning Question Generation +1

DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents

1 code implementation22 Oct 2022 Ziqiao Ma, Ben VanDerPloeg, Cristian-Paul Bara, Huang Yidong, Eui-In Kim, Felix Gervits, Matthew Marge, Joyce Chai

To this end, we introduce Dialogue On the ROad To Handle Irregular Events (DOROTHIE), a novel interactive simulation platform that enables the creation of unexpected situations on the fly to support empirical studies on situated communication with autonomous driving agents.

Autonomous Driving Dialogue Act Classification +2

How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus

1 code implementation SIGDIAL (ACL) 2021 Felix Gervits, Antonio Roque, Gordon Briggs, Matthias Scheutz, Matthew Marge

Intelligent agents that are confronted with novel concepts in situated environments will need to ask their human teammates questions to learn about the physical world.

Novel Concepts Question Generation +1

Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop

no code implementations11 Nov 2020 Matthew Marge, Carol Espy-Wilson, Nigel Ward

Fourth, more powerful adaptation methods are needed, to enable robots to communicate in new environments, for new tasks, and with diverse user populations, without extensive re-engineering or the collection of massive training data.

Dialogue-AMR: Abstract Meaning Representation for Dialogue

no code implementations LREC 2020 Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron artstein, David Traum, Clare Voss

This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems.

Natural Language Understanding

Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue

no code implementations10 Mar 2017 Matthew Marge, Claire Bonial, Brendan Byrne, Taylor Cassidy, A. William Evans, Susan G. Hill, Clare Voss

Our overall program objective is to provide more natural ways for soldiers to interact and communicate with robots, much like how soldiers communicate with other soldiers today.

Dialogue Management Management +1

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