PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation

Existing work on controlled text generation (CTG) assumes a control interface of categorical attributes. In this work, we propose a natural language 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. We design tailored experiments to test model's generalization abilities. The results show our PCFG-based command generation approach is effective for handling unseen commands compared to fix-set templates, and our proposed NL models can effectively generalize to unseen attributes.