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

TL;DR

The paper introduces FAME-MT, a large dataset of 11.2 million European language translations labeled for formality, enabling better control of formality levels in machine translation models.

Contribution

It provides the largest dataset of formality annotations for machine translation, along with a proof-of-concept model to steer translation formality levels.

Findings

FAME-MT is the largest formality-annotated translation dataset.

The dataset is reliable for language register information.

A proof-of-concept model demonstrates controlled formality in translations.

Abstract

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/ .