People use language for various purposes. Apart from sharing information,
individuals may use it to express emotions or to show respect for another
person. In this paper, we focus on the formality level of machine-generated
translations and present FAME-MT -- a dataset consisting of 11.2 million
translations between 15 European source languages and 8 European target
languages classified to formal and informal classes according to target
sentence formality. This dataset can be used to fine-tune machine translation
models to ensure a given formality level for each European target language
considered. We describe the dataset creation procedure, the analysis of the
dataset's quality showing that FAME-MT is a reliable source of language
register information, and we present a publicly available proof-of-concept
machine translation model that uses the dataset to steer the formality level of
the translation. Currently, it is the largest dataset of formality annotations,
with examples expressed in 112 European language pairs. The dataset is
published online: this https URL .

该研究关注机器生成翻译的正式程度，并介绍了 FAME-MT 数据集，该数据集包含 112 个欧洲语言对之间的 1120 万个翻译样本，旨在通过该数据集在欧洲目标语言中调整机器翻译模型的正式程度。

FAME-MT 数据集：机器翻译中简化形式意识的实现

FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation  Purposes

We propose the on-the-fly ensembling of a machine translation model with an
LLM, prompted on the same task and input. We perform experiments on 4 language
pairs (both directions) with varying data amounts. We find that a slightly
weaker-at-translation LLM can improve translations of a NMT model, and
ensembling with an LLM can produce better translations than ensembling two
stronger MT models. We combine our method with various techniques from LLM
prompting, such as in context learning and translation context.

我们在相同任务和输入上提出了一个机器翻译模型和 LLM 的即时集成方法。我们在 4 个语言对（两个方向）上进行了实验，并且数据量有所不同。我们发现，稍微差一些的 LLM 可以提高 NMT 模型的翻译质量，并且与 LLM 集成可以产生比两个更强的机器翻译模型集成更好的翻译结果。我们结合了 LLM 提示的各种技术，如上下文学习和翻译上下文。