Taylor A. Hudson


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SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus
Stephanie M. Lukin | Claire Bonial | Matthew Marge | Taylor A. Hudson | Cory J. Hayes | Kimberly Pollard | Anthony Baker | Ashley N. Foots | Ron Artstein | Felix Gervits | Mitchell Abrams | Cassidy Henry | Lucia Donatelli | Anton Leuski | Susan G. Hill | David Traum | Clare Voss
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

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.

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PropBank-Powered Data Creation: Utilizing Sense-Role Labelling to Generate Disaster Scenario Data
Mollie Frances Shichman | Claire Bonial | Taylor A. Hudson | Austin Blodgett | Francis Ferraro | Rachel Rudinger
Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024

For human-robot dialogue in a search-and-rescue scenario, a strong knowledge of the conditions and objects a robot will face is essential for effective interpretation of natural language instructions. In order to utilize the power of large language models without overwhelming the limited storage capacity of a robot, we propose PropBank-Powered Data Creation. PropBank-Powered Data Creation is an expert-in-the-loop data generation pipeline which creates training data for disaster-specific language models. We leverage semantic role labeling and Rich Event Ontology resources to efficiently develop seed sentences for fine-tuning a smaller, targeted model that could operate onboard a robot for disaster relief. We developed 32 sentence templates, which we used to make 2 seed datasets of 175 instructions for earthquake search and rescue and train derailment response. We further leverage our seed datasets as evaluation data to test our baseline fine-tuned models.