While much natural language processing work focuses on analyzing language content, language style also conveys important information about the situational context and purpose of communication. When editing an article, professional editors take into account the target audience to select appropriate word choice and grammar. Similarly, professional translators translate documents for a specific audience and often ask what is the expected tone of the content when taking a translation job.

Computational models of natural language should consider both their meaning and style. Controlling style is an emerging research area in text rewriting and is under-investigated in machine translation. In this dissertation, we present a new perspective which closely connects formality transfer and machine translation: we aim to control style in language generation with a focus on rewriting English or translating French to English with a desired formality. These are challenging tasks because annotated examples of style transfer are only available in limited quantities.

We first address this problem by inducing a lexical formality model based on word embeddings and a small number of representative formal and informal words. This enables us to assign sentential formality scores and rerank translation hypotheses whose formality scores are closer to user-provided formality level. To capture broader formality changes, we then turn to neural sequence to sequence models. Joint modeling of formality transfer and machine translation enables formality control in machine translation without dedicated training examples. Along the way, we also improve low-resource neural machine translation.