Many approaches to Natural Language Processing (NLP) tasks often treat them
as single-step problems, where an agent receives an instruction, executes it,
and is evaluated based on the final outcome. However, human language is
inherently interactive, as evidenced by the back-and-forth nature of human
conversations. In light of this, we posit that human-AI collaboration should
also be interactive, with humans monitoring the work of AI agents and providing
feedback that the agent can understand and utilize. Further, the AI agent
should be able to detect when it needs additional information and proactively
ask for help. Enabling this scenario would lead to more natural, efficient, and
engaging human-AI collaborations.
In this work, we explore these directions using the challenging task defined
by the IGLU competition, an interactive grounded language understanding task in
a MineCraft-like world. We explore multiple types of help players can give to
the AI to guide it and analyze the impact of this help in AI behavior,
resulting in performance improvements.

探索交互式人工智能与人类协作的方向，研究在基于 Minecraft 世界的交互式语言理解任务中，人类可以提供哪些类型的辅助帮助，以提高 AI 行为的性能和表现。