FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation Purposes
This addresses the need for formality-aware machine translation for users requiring culturally appropriate or context-specific language output, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of controlling formality levels in machine translation by creating FAME-MT, a dataset of 11.2 million translations across 15 source and 8 target European languages, classified as formal or informal, and demonstrated its use in fine-tuning models to steer translation formality.
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/ .