Papers by Matthew Marge

6 papers
Dialogue-AMR: Abstract Meaning Representation for Dialogue (2020.lrec-1)

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Challenge: Abstract Meaning Representation (AMR) does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context.
Approach: They propose a schema that enriches Abstract Meaning Representation (AMR) it provides a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems.
Outcome: The proposed schema provides a semantic representation for facilitating Natural Language Understanding (NLU) in human-robot dialogue systems.
ScoutBot: A Dialogue System for Collaborative Navigation (P18-4)

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Challenge: Demo will allow users to issue unconstrained spoken language commands to ScoutBot.
Approach: The demonstration will allow users to issue unconstrained spoken language commands to ScoutBot.
Outcome: The demonstration will allow users to issue unconstrained spoken language commands to ScoutBot.
Dialogue Structure Annotation for Multi-Floor Interaction (L18-1)

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Challenge: Existing annotation schemes do not address dialogue structure.
Approach: They propose an annotation scheme for meso-level dialogue structure that clusters utterances from multiple participants and floors into units according to realization of an initiator's intent.
Outcome: The proposed annotation scheme is used to annotate a corpus of human-robot interaction dialogues.
DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents (2022.findings-emnlp)

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Challenge: Empowering autonomous driving agents with the ability to navigate in a continuous and dynamic environment is critical.
Approach: They propose 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.
Outcome: The proposed platform enables the creation of unexpected situations on the fly to support empirical studies on situated communication with autonomous driving agents.
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus (2024.lrec-main)

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Challenge: The corpus contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue.
Approach: They present the Situated Corpus Of Understanding Transactions, a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration.
Outcome: The Situated Corpus Of Understanding Transactions (SCOUT) contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue.
A Research Platform for Multi-Robot Dialogue with Humans (N19-4)

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Challenge: a new research platform supports spoken dialogue interaction with multiple robots . a ground robot and an aerial robot are used to perform search and rescue tasks .
Approach: They propose a platform that supports spoken dialogue interaction with multiple robots . they use existing tools for speech recognition and dialogue management .
Outcome: The proposed platform supports spoken dialogue interaction with multiple robots in a search and rescue scenario.

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