Over the last few years, there has been growing interest in learning models
for physically grounded language understanding tasks, such as the popular
blocks world domain. These works typically view this problem as a single-step
process, in which a human operator gives an instruction and an automated agent
is evaluated on its ability to execute it. In this paper we take the first step
towards increasing the bandwidth of this interaction, and suggest a protocol
for including advice, high-level observations about the task, which can help
constrain the agent's prediction. We evaluate our approach on the blocks world
task, and show that even simple advice can help lead to significant performance
improvements. To help reduce the effort involved in supplying the advice, we
also explore model self-generated advice which can still improve results.

本研究通过提出一个包含建议的协议来增加物理基础语言理解任务的交互带宽，并在块世界任务上评估了该方法的有效性，发现简单的建议可以显著提高任务性能，同时也探讨了模型自动生成建议以减少提供建议的工作量。