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 https://github.com/IvanWang0730/StyleAP.
翻译:神经机器翻译(NMT) 由于其质量令人印象深刻, 引起了广泛的关注。 除了质量, 控制翻译风格也是许多语言的重要需求。 以前的相关研究主要侧重于控制形式, 并取得一些改进。 但是, 它们仍面临两个挑战。 首先是评估限制。 样式包含大量信息, 包括地名录、 语法等。 但只有形式问题得到了很好的研究。 第二个是, 在需要新风格时, 大量依赖迭代微调。 与此相对地, 本文在基准和方法方面有所贡献。 首先, 我们再次访问这一任务, 并提议一个多路双向机器翻译( MMSMT) 基准, 其中包括四种语言方向的多种类型风格, 以推展这项工作的界限。 其次, 我们提出一种名为风格激活( StyleAP) 的方法, 其方法是重新检索由标准化的单语道提供的提示, 不需要额外的微调。 实验显示StyAP 可以有效控制翻译风格并实现显著的性能。 我们的所有数据和代码都在 https://github. com/ IvanAP0730/Scodelearlate 。