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, which are not user-friendly. Furthermore, an inference gap
exists, as control specifications are only available during training but not
inference. In this work, we propose a new setup for controlled paraphrasing.
Specifically, we represent user-intended actions as action tokens, allowing
embedding and concatenating them with text embeddings, thus flowing together to
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 when control specifications are
inaccessible. Experimental results show that our method successfully enables
specific action-controlled paraphrasing and preserves the same or even better
performance compared to conventional uncontrolled methods when actions are not
given. Our findings thus promote the concept of optional action control for a
more user-centered design via representation learning.

通过引入操作令牌作为用户预期行为的表示，结合文本嵌入进行表示融合，以实现受控改写；实验结果表明，在不给定操作时，我们的方法能够成功实现特定操作受控的改写，并与传统的非受控方法保持相同甚至更好的性能，从而促进了面向用户的可选操作控制概念。