  Automatic dubbing (AD) is among the machine translation (MT) use cases where
translations should match a given length to allow for synchronicity between
source and target speech. For neural MT, generating translations of length
close to the source length (e.g. within +-10% in character count), while
preserving quality is a challenging task. Controlling MT output length comes at
a cost to translation quality, which is usually mitigated with a two step
approach of generating N-best hypotheses and then re-ranking based on length
and quality. This work introduces a self-learning approach that allows a
transformer model to directly learn to generate outputs that closely match the
source length, in short Isometric MT. In particular, our approach does not
require to generate multiple hypotheses nor any auxiliary ranking function. We
report results on four language pairs (English - French, Italian, German,
Spanish) with a publicly available benchmark. Automatic and manual evaluations
show that our method for Isometric MT outperforms more complex approaches
proposed in the literature.
