  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.
