  Automatic lyrics generation has received attention from both music and AI
communities for years. Early rule-based approaches have~---due to increases in
computational power and evolution in data-driven models---~mostly been replaced
with deep-learning-based systems. Many existing approaches, however, either
rely heavily on prior knowledge in music and lyrics writing or oversimplify the
task by largely discarding melodic information and its relationship with the
text. We propose an end-to-end melody-conditioned lyrics generation system
based on Sequence Generative Adversarial Networks (SeqGAN), which generates a
line of lyrics given the corresponding melody as the input. Furthermore, we
investigate the performance of the generator with an additional input
condition: the theme or overarching topic of the lyrics to be generated. We
show that the input conditions have no negative impact on the evaluation
metrics while enabling the network to produce more meaningful results.
