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Computational Models of Miscommunication Phenomena.

Matthew Purver1, Julian Hough2, Christine Howes3

  • 1Cognitive Science Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London.

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Summary
This summary is machine-generated.

This study introduces new computational models for detecting self- and other-repair in human conversations. These models improve communication quality analysis for applications like call centers and mental health monitoring.

Keywords:
DialogueDisfluencyIncrementalityMiscommunicationParallelismRepairSparsity

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Area of Science:

  • Computational linguistics
  • Dialogue systems
  • Human-computer interaction

Background:

  • Miscommunication phenomena like dialogue repair indicate communication quality.
  • Existing computational models for repair are often unsuitable for analyzing human-human dialogue.
  • Current models focus on specific repair types or require domain-specific knowledge.

Purpose of the Study:

  • To develop more satisfactory computational models for detecting self- and other-repair.
  • To address limitations of existing models in handling the full range of human repair phenomena.
  • To create tools for characterizing communication quality in diverse dialogue applications.

Main Methods:

  • Developed adapted models for self- and other-repair detection.
  • Focused on requirements such as incremental processing and robustness to data sparsity.
  • Evaluated models on datasets from various dialogue genres and domains.

Main Results:

  • Achieved promising results in detecting both self- and other-repair.
  • Demonstrated model effectiveness across different dialogue types.
  • Validated the suitability of the developed models for general use.

Conclusions:

  • The developed models meet key requirements for analyzing human dialogue repair.
  • These advancements enable better tools for assessing communication quality.
  • The findings support applications in areas like call center management and mental health monitoring.