Interactive Grounded Language Understanding in a Collaborative Environment: Retrospective on Iglu 2022 Competition

Open Access
Authors
  • J. Kiseleva
  • Alexey Skrynnik
  • Artem Zholus
  • Shrestha Mohanty
  • Negar Arabzadeh
  • Marc-Alexandre Côté
  • M. Aliannejadi ORCID logo
  • Milagro Teruel
  • Z. Li
  • M. Burtsev
  • M. ter Hoeve
  • Zoya Volovikova
  • Aleksandr Panov
  • Yuxuan Sun
  • Kavya Srinet
  • Arthur Szlam
  • Ahmed Awadallah
  • Seungeun Rho
  • Taehwan Kwon
  • Daniel Wontae Nam
  • Felipe Bivort Haiek
  • Edwin Zhang
  • Linar Abdrazakov
  • Matthew Ho
  • Guo Qingyam
  • Jason Zhang
  • Zhibin Guo
Publication date 2023
Journal Proceedings of Machine Learning Research
Event Thirty-sixth Conference on Neural Information Processing Systems
Volume | Issue number 220
Pages (from-to) 204-216
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
Human intelligence possesses the extraordinary ability to adapt rapidly to new tasks and multi-modal environments. This capacity emerges at an early age, as humans acquire new skills and learn to solve problems by imitating others or following natural language instructions. To facilitate research in this area, we recently hosted the second \emph{IGLU: Interactive Grounded Language Understanding in a Collaborative Environment} competition. The primary objective of the competition is to address the challenge of creating interactive agents that can learn to solve complex tasks by receiving grounded natural language instructions in a collaborative environment. Given the complexity of this challenge, we divided it into two sub-tasks: first, deciding whether the provided grounded instruction requires clarification, and second, following a clear grounded instruction to complete the task description.
Document type Article
Note Proceedings of the NeurIPS 2022 Competitions Track , 28-9 December 2022, New Orleans, LA, Online
Language English
Published at https://doi.org/10.48550/arXiv.2205.13771
Published at https://proceedings.mlr.press/v220/kiseleva23a.html
Downloads
2205.13771v1 (Submitted manuscript)
kiseleva23a (Final published version)
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