Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics
This addresses the challenging task of song translation for music and language processing, but it is incremental as it builds on existing translation and alignment methods.
The paper tackles the problem of automatic song translation, which requires translating lyrics and aligning them with melody, by proposing a joint encoder-decoder framework called LTAG that simultaneously translates and aligns notes, achieving effectiveness in English-Chinese experiments.
Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic solution to automatic song translation by jointly modeling lyrics translation and lyrics-melody alignment. It is a novel encoder-decoder framework that can simultaneously translate the source lyrics and determine the number of aligned notes at each decoding step through an adaptive note grouping module. To address data scarcity, we commissioned a small amount of training data annotated specifically for this task and used large amounts of augmented data through back-translation. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluation.