  This paper describes the SLT-CDT-UoS group's submission to the first Special
Task on Formality Control for Spoken Language Translation, part of the IWSLT
2022 Evaluation Campaign. Our efforts were split between two fronts: data
engineering and altering the objective function for best hypothesis selection.
We used language-independent methods to extract formal and informal sentence
pairs from the provided corpora; using English as a pivot language, we
propagated formality annotations to languages treated as zero-shot in the task;
we also further improved formality controlling with a hypothesis re-ranking
approach. On the test sets for English-to-German and English-to-Spanish, we
achieved an average accuracy of .935 within the constrained setting and .995
within unconstrained setting. In a zero-shot setting for English-to-Russian and
English-to-Italian, we scored average accuracy of .590 for constrained setting
and .659 for unconstrained.
