利用激活提示控制神经机器翻译中的风格
Controlling Styles in Neural Machine Translation with Activation Prompt
AI总结：
本文针对神经机器翻译风格控制中评估困难和对增量调整过度依赖的问题，提出多路风格化机器翻译基准MSMT和无需微调的风格激活提示方法StyleAP，实验证明其能有效控制翻译风格并取得显著性能。
摘要：
在神经机器翻译（NMT）中控制风格已引起广泛关注，因为这对于提升用户体验至关重要。关于该主题的早期研究通常集中于调节正式程度，并在这一方面取得了一定进展。然而，这些研究仍面临两大挑战。第一个挑战是风格评估的困难。风格包含词汇、句法等多个方面，能够提供丰富的信息，但目前只有正式程度得到了深入研究。第二个挑战涉及对增量调整的过度依赖，尤其是在需要新风格时。为解决这两个挑战，本文提出了一个新的基准和方法。我们引入了一个多路风格化机器翻译（MSMT）基准，涵盖四个语言领域中的多种风格类别。随后，我们提出了一种名为风格激活提示（StyleAP）的方法，通过从风格化单语语料库中检索提示来实现，无需额外的微调。实验表明，StyleAP能够有效控制翻译风格，并取得显著性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
