  Some generative models for sequences such as music and text allow us to edit
only subsequences, given surrounding context sequences, which plays an
important part in steering generation interactively. However, editing
subsequences mainly involves randomly resampling subsequences from a possible
generation space. We propose a contextual latent space model (CLSM) in order
for users to be able to explore subsequence generation with a sense of
direction in the generation space, e.g., interpolation, as well as exploring
variations -- semantically similar possible subsequences. A context-informed
prior and decoder constitute the generative model of CLSM, and a context
position-informed encoder is the inference model. In experiments, we use a
monophonic symbolic music dataset, demonstrating that our contextual latent
space is smoother in interpolation than baselines, and the quality of generated
samples is superior to baseline models. The generation examples are available
online.
