Songs Across Borders: Singable and Controllable Neural Lyric Translation
Longshen Ou Xichu Ma Min-Yen Kan Ye Wang
The development of general-domain neural machine translation (NMT) methods
has advanced significantly in recent years, but the lack of naturalness and
musical constraints in the outputs makes them unable to produce singable lyric
translations. This paper bridges the singability quality gap by formalizing
lyric translation into a constrained translation problem, converting
theoretical guidance and practical techniques from translatology literature to
prompt-driven NMT approaches, exploring better adaptation methods, and
instantiating them to an English-Chinese lyric translation system. Our model
achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and
word boundary recall. In our subjective evaluation, our model shows a 75%
relative enhancement on overall quality, compared against naive fine-tuning
(Code available at https://github.com/Sonata165/ControllableLyricTranslation).