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

This work aims to produce translations that convey source language content at
a formality level that is appropriate for a particular audience. Framing this
problem as a neural sequence-to-sequence task ideally requires training
triplets consisting of a bilingual sentence pair labeled with target language
formality. However, in practice, available training examples are limited to
English sentence pairs of different styles, and bilingual parallel sentences of
unknown formality. We introduce a novel training scheme for multi-task models
that automatically generates synthetic training triplets by inferring the
missing element on the fly, thus enabling end-to-end training. Comprehensive
automatic and human assessments show that our best model outperforms existing
models by producing translations that better match desired formality levels
while preserving the source meaning.

本文介绍了一种使用多任务学习的新训练方法，通过自动生成合成的训练三元组，解决实际语言环境下缺乏适当数据的问题，从而实现端到端训练。经过全面的自动化和人工评估，我们的模型在保持源意义的同时，更好地匹配所需的正式程度水平，远优于现有模型。