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.

翻译：控制神经机器翻译中的风格已引起广泛关注，这对提升用户体验至关重要。早期研究主要集中于调节正式度，并在此领域取得了一定进展，但仍面临两大挑战。其一，风格评估存在困难。风格涵盖词汇、句法等多个方面，提供了丰富的信息，但现有研究仅深入探讨了正式度。其二，过度依赖增量调整，尤其在需要引入新风格时。为解决这两个问题，本文提出了新的基准和方法。我们引入了多风格机器翻译基准，包含四个语言领域的多样化风格类型。随后，提出了一种名为风格激活提示的方法，通过从单语风格语料库中检索提示实现风格控制，无需额外微调。实验表明，StyleAP能有效控制翻译风格并取得显著性能。