  Existing work on controlled text generation (CTG) assumes a control interface
of categorical attributes. In this work, we propose a natural language (NL)
interface, where we craft a PCFG to embed the control attributes into natural
language commands, and propose variants of existing CTG models that take
commands as input. In our experiments, we design tailored setups to test
model's generalization abilities. We find our PCFG-based command generation
approach is effective for handling unseen commands compared to fix-set
templates; our proposed NL models can effectively generalize to unseen
attributes, a new ability enabled by the NL interface, as well as unseen
attribute combinations. Interestingly, we discover that the simple conditional
generation approach, enhanced with our proposed NL interface, is a strong
baseline in those challenging settings.
