NeurIPS 2021 Competition IGLU: Interactive Grounded Language
Understanding in a Collaborative Environment
- URL: http://arxiv.org/abs/2110.06536v2
- Date: Fri, 15 Oct 2021 01:11:15 GMT
- Title: NeurIPS 2021 Competition IGLU: Interactive Grounded Language
Understanding in a Collaborative Environment
- Authors: Julia Kiseleva, Ziming Li, Mohammad Aliannejadi, Shrestha Mohanty,
Maartje ter Hoeve, Mikhail Burtsev, Alexey Skrynnik, Artem Zholus, Aleksandr
Panov, Kavya Srinet, Arthur Szlam, Yuxuan Sun, Katja Hofmann, Michel Galley,
Ahmed Awadallah
- Abstract summary: We propose IGLU: Interactive Grounded Language Understanding in a Collaborative Environment.
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community: Natural Language Understanding and Generation (NLU/G) and Reinforcement Learning (RL)
- Score: 71.11505407453072
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human intelligence has the remarkable ability to adapt to new tasks and
environments quickly. Starting from a very young age, humans acquire new skills
and learn how to solve new tasks either by imitating the behavior of others or
by following provided natural language instructions. To facilitate research in
this direction, we propose IGLU: Interactive Grounded Language Understanding in
a Collaborative Environment. The primary goal of the competition is to approach
the problem of how to build interactive agents that learn to solve a task while
provided with grounded natural language instructions in a collaborative
environment. Understanding the complexity of the challenge, we split it into
sub-tasks to make it feasible for participants.
This research challenge is naturally related, but not limited, to two fields
of study that are highly relevant to the NeurIPS community: Natural Language
Understanding and Generation (NLU/G) and Reinforcement Learning (RL).
Therefore, the suggested challenge can bring two communities together to
approach one of the important challenges in AI. Another important aspect of the
challenge is the dedication to perform a human-in-the-loop evaluation as a
final evaluation for the agents developed by contestants.
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