Neural Syntactic Preordering for Controlled Paraphrase Generation

Understand

Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization.

• Past approaches struggle to cover this space of paraphrase possibilities in an interpretable manner.
• Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly "reorder'' the source sentence and guide our neural paraphrasing model.
• First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model.