NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment

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.

References in corpus (7)

• Rainbow: Combining Improvements in Deep Reinforcement Learning
• Towards a Human-like Open-Domain Chatbot
• The Eighth Dialog System Technology Challenge
• ConvLab: Multi-Domain End-to-End Dialog System Platform
• ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)
• The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
• SIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform