How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus
Felix Gervits, Antonio Roque, Gordon Briggs, Matthias Scheutz, Matthew Marge
Abstract
Intelligent agents that are confronted with novel concepts in situated environments will need to ask their human teammates questions to learn about the physical world. To better understand this problem, we need data about asking questions in situated task-based interactions. To this end, we present the Human-Robot Dialogue Learning (HuRDL) Corpus - a novel dialogue corpus collected in an online interactive virtual environment in which human participants play the role of a robot performing a collaborative tool-organization task. We describe the corpus data and a corresponding annotation scheme to offer insight into the form and content of questions that humans ask to facilitate learning in a situated environment. We provide the corpus as an empirically-grounded resource for improving question generation in situated intelligent agents.- Anthology ID:
- 2021.sigdial-1.37
- Volume:
- Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- July
- Year:
- 2021
- Address:
- Singapore and Online
- Editors:
- Haizhou Li, Gina-Anne Levow, Zhou Yu, Chitralekha Gupta, Berrak Sisman, Siqi Cai, David Vandyke, Nina Dethlefs, Yan Wu, Junyi Jessy Li
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 353–359
- Language:
- URL:
- https://aclanthology.org/2021.sigdial-1.37
- DOI:
- 10.18653/v1/2021.sigdial-1.37
- Cite (ACL):
- Felix Gervits, Antonio Roque, Gordon Briggs, Matthias Scheutz, and Matthew Marge. 2021. How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 353–359, Singapore and Online. Association for Computational Linguistics.
- Cite (Informal):
- How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus (Gervits et al., SIGDIAL 2021)
- PDF:
- https://preview.aclanthology.org/landing_page/2021.sigdial-1.37.pdf
- Code
- USArmyResearchLab/ARL-HuRDL
- Data
- HuRDL