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Many real-world tasks involve a mixed-initiative setup, wherein humans and AIsystems collaboratively perform a task. While significant work has beenconducted towards enabling humans to specify, through language, exactly how anagent should complete a task (i.e., low-level specification), prior work lackson interpreting the high-level strategic intent of the human commanders.Parsing strategic intent from language will allow autonomous systems toindependently operate according to the user's plan without frequent guidance orinstruction. In this paper, we build a computational interface capable oftranslating unstructured language strategies into actionable intent in the formof goals and constraints. Leveraging a game environment, we collect a datasetof over 1000 examples, mapping language strategies to the corresponding goalsand 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 showthat our model (125M parameters) significantly outperforms ChatGPT for thistask (p < 0.05) in a low-data setting.

A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting