Abstract: Given a sentence (e.g., "I like mangoes") and a constraint (e.g., negative sentiment), the goal of controlled text generation is to produce a sentence that adapts the input sentence to meet the requirements of the constraint (e.g., "I hate mangoes"). Going beyond such simple constraints, recent works have started exploring the incorporation of complex syntactic-guidance as constraints in the task of controlled paraphrase generation. In these methods, syntactic-guidance is sourced from a separate exemplar sentence. However, these prior works have only utilized limited syntactic information available in the parse tree of the exemplar sentence. We address this limitation in the paper and propose Syntax Guided Controlled Paraphraser (SGCP), an end-to-end framework for syntactic paraphrase generation. We find that S GCP can generate syntax-conforming sentences while not compromising on relevance. We perform extensive automated and human evaluations over multiple real-world datasets to demonstrate the efficacy of SGCP over state-of-the-art baselines. To drive future research, we have made SGCP ’s source code available.