Conversational services are emerging as a new paradigm for accessing information by simply uttering questions in natural language, posing a whole new set of challenges to the design and engineering of information systems. Training conversational services to deal with the nuances of natural language often requires collecting a high-quality and diverse set of training samples (i.e., paraphrases). Traditional approaches such as hiring an expert or crowdsourcing involve data collection processes that are often costly and time-consuming. Automated paraphrase generation is a promising cost-effective and scalable approach to generating training samples. Current automatic techniques, however, tend to specialise in specific types of lexical or syntactic variations. As a result, generated paraphrases may not perform well in relevant quality aspects such as diversity and semantic relatedness. In this paper, we follow an approach inspired by services integration to address these issues and generate paraphrases in English that are semantically relevant and diverse. We propose an extensible and reusable pipeline that combines automatic paraphrasing techniques in a two-step process that first focus on i) leveraging the strengths of multiple techniques to generate the most diverse (and possibly noisy) set of paraphrases, to then ii) address common quality issues in a separate step. Through empirical evaluations we show the benefits of the two-step process design and of combining techniques for more balancing relevance and diversity.