  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: https://github.com/laniqo-public/fame-mt/ .
