  Many real-world tasks involve a mixed-initiative setup, wherein humans and AI
systems collaboratively perform a task. While significant work has been
conducted towards enabling humans to specify, through language, exactly how an
agent should complete a task (i.e., low-level specification), prior work lacks
on interpreting the high-level strategic intent of the human commanders.
Parsing strategic intent from language will allow autonomous systems to
independently operate according to the user's plan without frequent guidance or
instruction. In this paper, we build a computational interface capable of
translating unstructured language strategies into actionable intent in the form
of goals and constraints. Leveraging a game environment, we collect a dataset
of over 1000 examples, mapping language strategies to the corresponding goals
and constraints, and show that our model, trained on this dataset,
significantly outperforms human interpreters in inferring strategic intent
(i.e., goals and constraints) from language (p < 0.05). Furthermore, we show
that our model (125M parameters) significantly outperforms ChatGPT for this
task (p < 0.05) in a low-data setting.
