Neural machine translation(NMT) has aroused wide attention due to its
impressive quality. Beyond quality, controlling translation styles is also an
important demand for many languages. Previous related studies mainly focus on
controlling formality and gain some improvements. However, they still face two
challenges. The first is the evaluation limitation. Style contains abundant
information including lexis, syntax, etc. But only formality is well studied.
The second is the heavy reliance on iterative fine-tuning when new styles are
required. Correspondingly, this paper contributes in terms of the benchmark and
approach. First, we re-visit this task and propose a multiway stylized machine
translation (MSMT) benchmark, which includes multiple categories of styles in
four language directions to push the boundary of this task. Second, we propose
a method named style activation prompt (StyleAP) by retrieving prompts from
stylized monolingual corpus, which needs no extra fine-tuning. Experiments show
that StyleAP could effectively control the style of translation and achieve
remarkable performance. All of our data and code are released at
this https URL.

本文提出了一个包括多个语言方向和多个风格类别的多途径风格化机器翻译基准测试，并通过提取自风格化单语语料库的提示的方式，实现了对翻译风格的有效控制，从而取得了显著的性能。