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.

本论文提出了一种名为 “同构机器翻译” 的自学习方法，使得变压器模型直接学习生成与源文本长度接近的输出，而不需要产生多个假设或任何辅助排名函数，并在四种语言对上进行了测试，结果表明该方法优于文献中提出的更复杂的方法。