  This paper develops automatic song translation (AST) for tonal languages and
addresses the unique challenge of aligning words' tones with melody of a song
in addition to conveying the original meaning. We propose three criteria for
effective AST -- preserving meaning, singability and intelligibility -- and
design metrics for these criteria. We develop a new benchmark for
English--Mandarin song translation and develop an unsupervised AST system,
Guided AliGnment for Automatic Song Translation (GagaST), which combines
pre-training with three decoding constraints. Both automatic and human
evaluations show GagaST successfully balances semantics and singability.
