  Recent studies have demonstrated the potential to control paraphrase
generation, such as through syntax, which has broad applications in various
downstream tasks. However, these methods often require detailed parse trees or
syntactic exemplars, countering human-like paraphrasing behavior in language
use. Furthermore, an inference gap exists, as control specifications are only
available during training but not during inference. In this work, we propose a
new setup for controlled paraphrase generation. Specifically, we represent user
intent as action tokens, embedding and concatenating them with text embeddings,
thus flowing together into a self-attention encoder for representation fusion.
To address the inference gap, we introduce an optional action token as a
placeholder that encourages the model to determine the appropriate action
independently when users' intended actions are not provided. Experimental
results show that our method successfully enables precise action-controlled
paraphrasing and preserves or even enhances performance compared to
conventional uncontrolled methods when actions are not given. Our findings
promote the concept of action-controlled paraphrasing for a more user-centered
design.
