@inproceedings{lukin-etal-2024-scout-situated,
title = "{SCOUT}: A Situated and Multi-Modal Human-Robot Dialogue Corpus",
author = "Lukin, Stephanie M. and
Bonial, Claire and
Marge, Matthew and
Hudson, Taylor A. and
Hayes, Cory J. and
Pollard, Kimberly and
Baker, Anthony and
Foots, Ashley N. and
Artstein, Ron and
Gervits, Felix and
Abrams, Mitchell and
Henry, Cassidy and
Donatelli, Lucia and
Leuski, Anton and
Hill, Susan G. and
Traum, David and
Voss, Clare",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1259",
pages = "14445--14458",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus
%A Lukin, Stephanie M.
%A Bonial, Claire
%A Marge, Matthew
%A Hudson, Taylor A.
%A Hayes, Cory J.
%A Pollard, Kimberly
%A Baker, Anthony
%A Foots, Ashley N.
%A Artstein, Ron
%A Gervits, Felix
%A Abrams, Mitchell
%A Henry, Cassidy
%A Donatelli, Lucia
%A Leuski, Anton
%A Hill, Susan G.
%A Traum, David
%A Voss, Clare
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F lukin-etal-2024-scout-situated
%X 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.
%U https://aclanthology.org/2024.lrec-main.1259
%P 14445-14458
Markdown (Informal)
[SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus](https://aclanthology.org/2024.lrec-main.1259) (Lukin et al., LREC-COLING 2024)
ACL
- 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, and Clare Voss. 2024. SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14445–14458, Torino, Italia. ELRA and ICCL.