  In this paper, we propose to adapt the method of mutual information
maximization into the task of Chinese lyrics conditioned melody generation to
improve the generation quality and diversity. We employ scheduled sampling and
force decoding techniques to improve the alignment between lyrics and melodies.
With our method, which we called Diverse Melody Generation (DMG), a
sequence-to-sequence model learns to generate diverse melodies heavily
depending on the input style ids, while keeping the tonality and improving the
alignment. The experimental results of subjective tests show that DMG can
generate more pleasing and coherent tunes than baseline methods.
