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Enhancing Wizard-of-Oz Paradigm for Future Human-Machine Dialogues

This paper explores modernizing the Wizard-of-Oz paradigm to better align with upcoming human-machine dialogues by addressing key questions. We investigate enhancing MDP approaches for learning dialogue strategies from corpora, define essential dialogue states and actions, and aim for a more natural handling of speech understanding errors in automated systems. The application discussed centers around assisting Heiskell Library patrons with disabilities through an advanced automated readers advisor while employing contrastive conditions to refine dialogue interactions.

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Enhancing Wizard-of-Oz Paradigm for Future Human-Machine Dialogues

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  1. A WOZ Variant with Contrastive Conditions Esther Levin, City College of New York Rebecca Passonneau, Columbia University

  2. Motivating questions • How can we update the wizard-of-oz paradigm to resemble human/machine dialogues of the near future? • How can we enhance MDP approaches to learning dialogue strategies from corpora? • What dialogue states to define • What dialogue actions to define • How can the types of speech understanding errors made by systems be handled more naturally? Dialogue on Dialogues Workshop

  3. Application:Heiskell Library,Automated Readers Advisor Heiskell patrons: • cannot read “print” books • Blind, visually handicapped, physically disabled • request audio/braille/large-print books by telephone • request items by author, title or catalogue number • highly motivated, technologically adept, directly involved with the library Dialogue on Dialogues Workshop

  4. Approach:Wizard Ablation, Contrastive conditions • Ablation conditions • AW1: wizard input is ASR; wizard output is unconstrained • AW2: wizard input is ASR; wizard selects response actions from system actions • Two dimensions of contrast • user/librarian; user/AW1; user/AW2; user/system • pre/post system enrichment Dialogue on Dialogues Workshop

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