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SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus

Stephanie M. Lukin, Claire Bonial, Matthew Marge, Taylor Hudson, Cory J. Hayes, Kimberly A. Pollard, Anthony Baker, Ashley N. Foots, Ron Artstein, Felix Gervits, Mitchell Abrams, Cassidy Henry, Lucia Donatelli, Anton Leuski, Susan G. Hill, David Traum, Clare R. Voss

TL;DR

The Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration, is introduced and described how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots.

Abstract

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker's intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered.

SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus

TL;DR

The Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration, is introduced and described how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots.

Abstract

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker's intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered.

Paper Structure

This paper contains 14 sections, 7 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Experiments and Data Collection
  3. SCOUT Construction
  4. SCOUT Statistics
  5. Annotations on SCOUT
  6. Standard-AMR and Dialogue-AMR Annotation
  7. Dialogue Structure Annotation
  8. Applications
  9. Systems Developed from SCOUT Annotations
  10. Analyses of Human-Robot Multi-Modal Communication
  11. Related Work
  12. Conclusion
  13. Acknowledgements
  14. Bibliographical References

Figures (7)

  • Figure 1: Wizard-of-Oz data collection design
  • Figure 2: One LIDAR map and annotated floor plan with items scanned or not scanned by the LIDAR marked. Floor plan and legend were not shown during the exercise.
  • Figure 3: Tab delimited format for Dialogue Structure. Transaction Unit (TU), antecedent (ant).
  • Figure 4: Aligned .xlsx transcript screenshot format for Experiments 1-3
  • Figure 5: Aligned .xlsx transcript screenshot format for Experiment 4 with the ASR results and intermediary normalized forms
  • ...and 2 more figures