  Controlling styles in neural machine translation (NMT) has attracted wide
attention, as it is crucial for enhancing user experience. Earlier studies on
this topic typically concentrate on regulating the level of formality and
achieve some progress in this area. However, they still encounter two major
challenges. The first is the difficulty in style evaluation. The style
comprises various aspects such as lexis, syntax, and others that provide
abundant information. Nevertheless, only formality has been thoroughly
investigated. The second challenge involves excessive dependence on incremental
adjustments, particularly when new styles are necessary. To address both
challenges, this paper presents a new benchmark and approach. A multiway
stylized machine translation (MSMT) benchmark is introduced, incorporating
diverse categories of styles across four linguistic domains. Then, we propose a
method named style activation prompt (StyleAP) by retrieving prompts from
stylized monolingual corpus, which does not require extra fine-tuning.
Experiments show that StyleAP could effectively control the style of
translation and achieve remarkable performance.
