Formality style transfer (FST) automatically modifies a piece of text with respect to its level of formality, while conveying the same meaning. Existing models approach FST as a binary task, i.e., transforming an informal sentence into a formal sentence, or vice versa. This is a simplistic approach since different language contexts --- from text messages, social media posts, e-mails, to official documents --- require a range of formality levels, rather than a formal vs. informal dichotomy. This project expands the FST task from a dichotomy to a cline of formality in a relative paradigm. Our proposed model transforms the input sentence to imitate the formality level of its immediate context, such as the dialog history or discussion thread. This constitutes the first effort to leverage pairwise ranking models, which have been shown to be effective in other style transfer tasks, for FST.